ID2223 Lecture 2: Distributed ML and Linear Regression
|
|
- Meredith Sparks
- 5 years ago
- Views:
Transcription
1 ID2223 Lecture 2: Distributed ML and Linear Regression
2 Terminology Observations. Entities used for learning/evaluation Features. Attributes (typically numeric) used to represent an observation Labels. Values/categories assigned to observations Model. Parameters/weights that are adjusted by training to predict label(s) given observations. Training, Validation, and Test Data. Observations for training and evaluating a learning algorithm - Training data is given to the algorithm for training - Validation data is withheld from training and is used to measure the performance of training - Test data is withheld at train time ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 2/77
3 Supervised Learning Labelled Observations Input Data Supervised Learning Prediction Learn from labeled observations. Labels teach the algorithm to learn a mapping from observations to labels ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 3
4 Supervised Learning Classification. Assign a category to each item - Categories are discrete [Image from Sze et al, 2017] ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 4/77
5 Supervised Learning Regression. Predict a real value for each item - Labels are continuous - Can define closeness when comparing prediction with label ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 5/77
6 Unsupervised Learning List of Data Points Unsupervised Learning List of Cluster Labels Unsupervised Learning Clustering. Partition observations into homogeneous regions Dimensionality Reduction. Transform an initial feature representation into a more concise representation ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 6
7 Non-Distributed Representations Not Compositional x x x x x x x Regions defined by learned Prototypes - Nearest Neighbour - Decision Trees (DTs) Random Forests Gradient Boosted DTs - Clustering x [Bengio, BayArea DL School, 16] /58
8 Parametric Learning A learning model that summarizes data with a set of parameters of fixed size (independent of the number of training examples) is called a parametric model. No matter how much data you throw at a parametric model, it won t change its mind about how many parameters it needs. Artificial Intelligence: A Modern Approach, page ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 8/77
9 Parametric vs Non Parametric Learning Parametric = bounded number of parameters Non-parametric = unbounded number of parameters Parametric or non-parametric models affect - the way the learning is stored - the method for learning Possible Combinations: Supervised Parametric Unsupervised Parametric Supervised Non-Parametric Unsupervised Non-Parametric ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 9
10 Supervised Parametric Learning Imagine a machine where you can input some data, turn some dials and observe its performance - Output data is a prediction [Grokking Deep Learning, Manning 16]` 10
11 Supervised Parametric Learning Algorithm 1. Predict 2. Compare to actual result (true pattern) 3. Adjust the dials (parameters) to improve predictions [Grokking Deep Learning, Manning 16] 11
12 Machine Learning Pipeline ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 12/77
13 Machine Learning Hierarchy of Needs Prediction DDL (Distributed Deep Learning) Deep Learning, RL, Automated ML Labeled Data, ML Experimentation Analytics B.I. Analytics, Metrics, Aggregates, Features, Training/Test Data Reliable Data Pipelines, ETL, Unstructured and Structured Data Storage, Real-Time Data Ingestion ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 13/77
14 Importing Data Import the raw data (observations) from some source - Real-time data (Kafka) - Data-at-rest (HDFS) Different data formats likely Data may have duplicate, missing columns, invalid values - Clean and wrangle the data Store the data in a format that is efficient for querying (partitioned) Acquire Raw Data Clean/Partition Data ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 14/77
15 Feature Extraction Extract features to represent the observations - Exploit domain knowledge Acquire Raw Data Clean/Partition Data Nearly always want numeric features Feature Extraction Choice of features is crucial to the success of the entire pipeline ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 15/77
16 Supervised Learning Train a supervised model using labeled data Classification or Regression model Acquire Raw Data Clean/Partition Data Feature Extraction Supervised Learning ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 16/77
17 Learning Q: How do we determine the quality of the model we ve just trained? A: We can evaluate it on test / hold-out data, i.e., labeled data not used for training If we don t like the results, we iterate Acquire Raw Data Clean/Partition Data Feature Extraction Supervised Learning Evaluation ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 17/77
18 Predict Once we re happy with our model, we can use it to make predictions on future observations, i.e., data without a known label Acquire Raw Data Clean/Partition Data Feature Extraction Supervised Learning Evaluation Predict ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 18/77
19 Classification Goal: Learn a mapping from observations to discrete labels given a set of training examples (supervised learning) ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 19/77
20 Classification Examples Spam Classification - s {spam, ham} Anomaly detection - Network activity {anomoly, not anomoly} Fraud detection - Shopping activity {fraud, not fraud} Clickthrough rate prediction - User viewing an ad {click, no click} ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 20/77
21 Classification Example 20 Newsgroups 1000s of documents from 20 Usenet Newsgroups - alt.atheism, soc.religion.christian, talk.politics.guns - comp.sys.ibm.pc.hardware, comp.sys.mac.hardware Train a model to classify documents from the 20 Newsgroups data set into two categories according to whether or not the documents are computer related. Goal: classify each document as: IS_COMPUTERS or NOT_COMPUTERS ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 21/77
22 Classification Pipeline Newsgroups20 Acquire Raw Data Raw data consists of a set of labeled training observations > >>I have been at a shooting range where >>gang members were "practicing" shooting. Feature Extraction Supervised Learning Evaluation Predict In article <C5qsBF.IEK@ms.uky.edu> billq@ms.uky.edu (Billy Quinn) writes: >I built a little project using the radio shack 5vdc relays to switch > ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 22/77
23 Classification Pipeline Observation talk.politics.guns/54279 > >>I have been at a shooting range where >>gang members were "practicing" shooting. Label NOT_COMPUTERS Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict sci.electronics/53909 In article <C5qsBF.IEK@ms.uky.edu> billq@ms.uky.edu (Billy Quinn) writes: >I built a little project using the radio shack 5vdc relays to switch >. IS_COMPUTERS ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 23/77
24 Classification Pipeline > >>I have been at a shooting range where >>gang members were "practicing" shooting. Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict Feature extraction usually converts each observation into a vector of real numbers (features) Choosing a good description for an observation will have huge baring on the success or failure of a classifier ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 24/77
25 Classification Pipeline case class newsgroupscaseclass (id: String, text: String, topic: String) Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 25/77
26 Classification Pipeline Show only documents that are related to computer topics Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 26/77
27 Classification Pipeline > >>I have been at a shooting range where >>gang members were "practicing" shooting. Classifier Acquire Raw Data Feature Extraction Supervised Learning Evaluation Supervised Learning: Train classifier using training data - Common classifiers include Logistic Regression, SVMs, Decision Trees, Random Forests, etc. Training (especially at scale) often involves iterative computations, e.g., gradient descent Predict ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 27/77
28 Logistic Regression Goal: Find linear decision boundary - Parameters to learn are feature weights and offset - Nice probabilistic interpretation - Covered in more detail later in course Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict [Distributed Machine Learning with Apache Spark, Berkeley 16 ] /77
29 Evaluation How can we evaluate the quality of our classifier? We want good predictions on unobserved data - Generalization ability Accuracy on training data is overly optimistic since classifier has already learned from it - We might be overfitting Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 29/77
30 Overfitting and Generalization Fitting training data does not guarantee generalization, e.g., lookup table Which figure below is a better classifier? - Left: perfectly fits training samples, but it is complex and overfits the data Favour simple models over complex ones, ceteris paribus Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict /77 [Distributed Machine Learning with Apache Spark, Berkeley 16 ]
31 Classification Pipeline How can we evaluate the quality of our classifier? Idea: Create test set to simulate unobserved data Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict Evaluation: Split dataset into training / testing datasets - Various ways to compare predicted and true labels - Evaluation criterion is called a loss function - Accuracy (or 0-1 loss) is common for classification ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 31/77
32 Classification Pipeline Split data set into separate training (70%) and test (30%) data sets Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict [Distributed Machine Learning with Apache Spark, Berkeley 16 ] /77
33 Classification Pipeline Predict: Final classifier can then be used to make predictions on future observations Acquire Raw Data Feature Extraction Supervised Learning Evaluation Predict [Distributed Machine Learning with Apache Spark, Berkeley 16 ] /77
34 Linear Regression ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 34/77
35 Regression Goal: Learn a mapping from observations (features) to continuous values/labels given a training set (supervised learning) ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 35/77
36 Linear Least Squares Regression For each observation we have a feature vector, x, and label, y x T = x 1 x 2 x d We assume a linear mapping between features and label: y w o + w 1 x 1 + w d 1 x d ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 36/77
37 Linear Least Squares Regression We can augment the feature vector to incorporate offset: x T = 1 x 1 x 2 x d We can then rewrite this linear mapping as a scalar product: y y = d i=0 w i x i = w T x ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 37/77
38 Least Squares Regression Given n training samples with d features, we define: X R n d : matrix storing points y R n : real-valued labels y R n : predicted labels, where y = Xw w R d : model parameters ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 38/77
39 Evaluating Predictions What is an appropriate evaluation metric or loss function? Absolute loss: Squared loss: y y y y ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 39/77
40 Learning a Model an Optimization Problem Assume we have n training points, where x (i) denotes the ith point Idea: Find the model w that minimizes squared loss over the training points: min w n i=1 w T x i y i 2 where y = w T x i ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 40/77
41 Least Squares Regression Least Squares Regression: Learn the mapping (w) from features to labels that minimizes residual sum of squares: min w Xw y 2 2 Equivalent by definition of the Euclidean norm: min w n i=1 w T x i y i ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 41/77
42 Closed Form Solution Solve by setting derivative to zero - (left as exercise) - Hint: find the minimum by differentiating the loss (error) function and setting to zero. Then you will need two linear algebra steps. Solution: w = ( X T X ) 1 X T y ( X T X ) 1 should not be zero ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 42/77
43 Overfitting and Generalization We want good predictions on new data, i.e., generalization Least squares regression minimizes training error, and could overfit - Simpler models are more likely to generalize (Occam s razor) - Can we change the problem to penalize for model complexity? - Intuitively, models with smaller weights are simpler Can we penalize overly complex models? Ridge Regression does this by adding a regularization term ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 43/77
44 Ridge Regression Ridge Regression: Learn mapping (w) that minimizes the residual sum of squares along with a regularization term: Training Error Model Complexity min w Xw y λ w 2 2 Free parameter that trades off training error and model complexity ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 44/77
45 Hyperparameters λ w 2 2 How do find good values for this hyperparameter? - Need to tune hyperparameters Problem: If we try out different hyperparameter values and evaluate them using the test set, there is a danger of overfitting the data. Why?? - Problem arises as the test set should reflect unobserved data Solution: Use a 2 nd hold out dataset to evaluate different tunings for hyperparameters ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 45/77
46 Evaluating with the Validation Set Hyperparameter tuning - Training: train different models - Validation: evaluate different models - Test: evaluate the accuracy of the final model [Distributed Machine Learning with Apache Spark, Berkeley 16 ] /77
47 Hyperparameter Search Grid Search: Exhaustively search through hyperparameter space - Define and discretize search space (linear or log scale) ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 47/77 [Distributed Machine Learning with Apache Spark, Berkeley 16 ]
48 Evaluation Evaluate final model - Training set: train various models - Validation set: evaluate various models - Test set: evaluate final model s accuracy [Distributed Machine Learning with Apache Spark, Berkeley 16 ] 49/77
49 Predict The trained model can then be used to make predictions on unseen observations [Distributed Machine Learning with Apache Spark, Berkeley 16 ] /77
50 Distributed Machine Learning ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 51/77
51 Computational Complexity - Review ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 52/77
52 Time Complexity The (worst-case) time complexity of a problem is the time complexity of the fastest algorithm that solves the problem. Ο(n): there is an algorithm that solves the problem faster than this time - Has to be true for all possible inputs, that is, the worst-case What is the time complexity of the following code snippet? for ( i=0 ; i<n ; i++ ) for( j=0 ; j<n ; j++ ) for( k=0 ; k<n ; k++ ) sum[i][j] += entry[i][j][k]; 53
53 Orders-of-Growth ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 54/77
54 Common Orders-of-Growth O(1) constant O(log(n)) logarithmic time O(n) linear O(n log(n)) linearithmic time O(n 2 ) quadratic time O(2 n ) exponential time ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 55/77
55 Orders-of-Growth Example ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 56/77
56 f x = O g x Big-Oh (O) - ignore constant and lower-order terms Big-Oh determines the upper bound on a function f n = O g n iff c > 0 and n 0 > 0 where f n cg n n n /77
57 Space Complexity The space complexity of an algorithm is an expression for the worst-case amount of memory that the algorithm will use. 58/77
58 Linear Regression:Computational Complexity Can we count the number of arithmetic operations for? - Assume we have d features and n samples w = ( X T X ) 1 X T y ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 59/77
59 Linear Regression:Computational Complexity If we count the number of arithmetic operations for: w = ( X T X ) 1 X T y Cost of operations (n is #samples, d is #features): - X T X : O(nd 2 ) - Matrix inverse of X T X: O(d 3 ) - X T y : O(nd) - Product X T y and Inverse Matrix: O(d 2 ) The Time Complexity is: O(nd 2 + d 3 ) Ignore lower-order terms ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 60/77
60 Linear Regression: Space Complexity Storage costs: X T X and its inverse: O(d 2 ) floats X : O(nd) floats Space complexity: O(nd + d 2 ) floats ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 61/77
61 Big n and Small d Assume O(d 3 ) computation and O(d 2 ) memory is feasible on a single machine Storing X and computing X T X are the bottlenecks How can we distribute storage and computation? - Partition rows of X over hosts - Compute X T X as a sum of outer products ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 62/77
62 Matrix Multiplication using Inner Products Each entry of output matrix is result of inner product of inputs matrices ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 63/77
63 Matrix Multiplication using Inner Products = = ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 64/77
64 Matrix Multiplication using Inner Products = = ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 65/77
65 Matrix Multiplication using Inner Products = = ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 66/77
66 Matrix-Matrix Multiplication = = ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 67/77
67 Matrix Multiplication via Outer Products Output matrix is the sum of outer products between corresponding rows and columns of input matrices = ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 68/77
68 Matrix Multiplication via Outer Products Output matrix is the sum of outer products between corresponding rows and columns of input matrices = ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 69/77
69 Matrix Multiplication via Outer Products Output matrix is the sum of outer products between corresponding rows and columns of input matrices = = ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 70/77
70 Computational Complexity with Map-Reduce ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 71/77 [Distributed Machine Learning with Apache Spark, Berkeley 16 ]
71 Computational Complexity with Map-Reduce ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 72/77 [Distributed Machine Learning with Apache Spark, Berkeley 16 ]
72 Big n and Big d With big d, storing and computing are still the bottlenecks O(d 3 ) computation in the reduce step is not easily parallelized
73 Big n and Big d not Computationally Feasible Distributing storage and processing doesn t change the cubic computational complexity Options to scale include: 1. Exploit data sparsity to reduce dimensionality or 2. Make computation (and storage) linear in (n, d) ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 74/77
74 Iterative Algorithms are more Efficient We need methods that are linear in time and space Gradient descent is an iterative algorithm that requires O(nd) computation and O(d) local storage per iteration ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 75/77
75 Gradient Descent [Distributed Machine Learning with Apache Spark, Berkeley 16 ] /77
76 References Distributed Machine Learning with Apache Spark, UCLA/Berkeley Course 2016 Andrew Ng, Machine Learning CS 229, Stanford Deep Learning Book, Section 4.5 Sze et Al, Efficient Processing of Deep Neural Networks: A Tutorial and Survey, ID2223, Large Scale Machine Learning and Deep Learning, Jim Dowling 77/77
Lecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More information(Sub)Gradient Descent
(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationCS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationAssignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationIntroduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
More informationMachine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler
Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina
More informationGenerative models and adversarial training
Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?
More informationSystem Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationStatewide Framework Document for:
Statewide Framework Document for: 270301 Standards may be added to this document prior to submission, but may not be removed from the framework to meet state credit equivalency requirements. Performance
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationUnsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model
Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.
More informationMathematics. Mathematics
Mathematics Program Description Successful completion of this major will assure competence in mathematics through differential and integral calculus, providing an adequate background for employment in
More informationTwitter Sentiment Classification on Sanders Data using Hybrid Approach
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders
More informationModel Ensemble for Click Prediction in Bing Search Ads
Model Ensemble for Click Prediction in Bing Search Ads Xiaoliang Ling Microsoft Bing xiaoling@microsoft.com Hucheng Zhou Microsoft Research huzho@microsoft.com Weiwei Deng Microsoft Bing dedeng@microsoft.com
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationLinking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
More informationSwitchboard Language Model Improvement with Conversational Data from Gigaword
Katholieke Universiteit Leuven Faculty of Engineering Master in Artificial Intelligence (MAI) Speech and Language Technology (SLT) Switchboard Language Model Improvement with Conversational Data from Gigaword
More informationLecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
More informationAxiom 2013 Team Description Paper
Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association
More informationHonors Mathematics. Introduction and Definition of Honors Mathematics
Honors Mathematics Introduction and Definition of Honors Mathematics Honors Mathematics courses are intended to be more challenging than standard courses and provide multiple opportunities for students
More information*Net Perceptions, Inc West 78th Street Suite 300 Minneapolis, MN
From: AAAI Technical Report WS-98-08. Compilation copyright 1998, AAAI (www.aaai.org). All rights reserved. Recommender Systems: A GroupLens Perspective Joseph A. Konstan *t, John Riedl *t, AI Borchers,
More informationCSL465/603 - Machine Learning
CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am
More informationAP Calculus AB. Nevada Academic Standards that are assessable at the local level only.
Calculus AB Priority Keys Aligned with Nevada Standards MA I MI L S MA represents a Major content area. Any concept labeled MA is something of central importance to the entire class/curriculum; it is a
More informationRule Learning with Negation: Issues Regarding Effectiveness
Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX
More informationSemi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.
Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More informationGrade 6: Correlated to AGS Basic Math Skills
Grade 6: Correlated to AGS Basic Math Skills Grade 6: Standard 1 Number Sense Students compare and order positive and negative integers, decimals, fractions, and mixed numbers. They find multiples and
More informationArtificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14
More informationDeep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach
#BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying
More informationUsing Web Searches on Important Words to Create Background Sets for LSI Classification
Using Web Searches on Important Words to Create Background Sets for LSI Classification Sarah Zelikovitz and Marina Kogan College of Staten Island of CUNY 2800 Victory Blvd Staten Island, NY 11314 Abstract
More informationExploration. CS : Deep Reinforcement Learning Sergey Levine
Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?
More informationChinese Language Parsing with Maximum-Entropy-Inspired Parser
Chinese Language Parsing with Maximum-Entropy-Inspired Parser Heng Lian Brown University Abstract The Chinese language has many special characteristics that make parsing difficult. The performance of state-of-the-art
More informationComment-based Multi-View Clustering of Web 2.0 Items
Comment-based Multi-View Clustering of Web 2.0 Items Xiangnan He 1 Min-Yen Kan 1 Peichu Xie 2 Xiao Chen 3 1 School of Computing, National University of Singapore 2 Department of Mathematics, National University
More informationLaboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning
More informationActive Learning. Yingyu Liang Computer Sciences 760 Fall
Active Learning Yingyu Liang Computer Sciences 760 Fall 2017 http://pages.cs.wisc.edu/~yliang/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Mark Craven,
More informationThe Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
More informationCLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH
ISSN: 0976-3104 Danti and Bhushan. ARTICLE OPEN ACCESS CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH Ajit Danti 1 and SN Bharath Bhushan 2* 1 Department
More informationarxiv: v1 [cs.lg] 15 Jun 2015
Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy arxiv:1506.04477v1 [cs.lg] 15 Jun 2015 Sang-Woo Lee Min-Oh Heo School of Computer Science and
More informationLearning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models
Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za
More informationCalibration of Confidence Measures in Speech Recognition
Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE
More informationKnowledge Transfer in Deep Convolutional Neural Nets
Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract
More informationQuickStroke: An Incremental On-line Chinese Handwriting Recognition System
QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents
More informationNumeracy Medium term plan: Summer Term Level 2C/2B Year 2 Level 2A/3C
Numeracy Medium term plan: Summer Term Level 2C/2B Year 2 Level 2A/3C Using and applying mathematics objectives (Problem solving, Communicating and Reasoning) Select the maths to use in some classroom
More informationSemi-Supervised Face Detection
Semi-Supervised Face Detection Nicu Sebe, Ira Cohen 2, Thomas S. Huang 3, Theo Gevers Faculty of Science, University of Amsterdam, The Netherlands 2 HP Research Labs, USA 3 Beckman Institute, University
More informationGiven a directed graph G =(N A), where N is a set of m nodes and A. destination node, implying a direction for ow to follow. Arcs have limitations
4 Interior point algorithms for network ow problems Mauricio G.C. Resende AT&T Bell Laboratories, Murray Hill, NJ 07974-2070 USA Panos M. Pardalos The University of Florida, Gainesville, FL 32611-6595
More informationGuide to the Uniform mark scale (UMS) Uniform marks in A-level and GCSE exams
Guide to the Uniform mark scale (UMS) Uniform marks in A-level and GCSE exams This booklet explains why the Uniform mark scale (UMS) is necessary and how it works. It is intended for exams officers and
More informationCS 446: Machine Learning
CS 446: Machine Learning Introduction to LBJava: a Learning Based Programming Language Writing classifiers Christos Christodoulopoulos Parisa Kordjamshidi Motivation 2 Motivation You still have not learnt
More informationLearning From the Past with Experiment Databases
Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University
More informationUniversity of Groningen. Systemen, planning, netwerken Bosman, Aart
University of Groningen Systemen, planning, netwerken Bosman, Aart IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document
More informationExperiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling
Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad
More informationReducing Features to Improve Bug Prediction
Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science
More informationThe 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X
The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,
More informationSoftprop: Softmax Neural Network Backpropagation Learning
Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science
More informationCS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University
CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE Mingon Kang, PhD Computer Science, Kennesaw State University Self Introduction Mingon Kang, PhD Homepage: http://ksuweb.kennesaw.edu/~mkang9
More informationAGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS
AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS 1 CALIFORNIA CONTENT STANDARDS: Chapter 1 ALGEBRA AND WHOLE NUMBERS Algebra and Functions 1.4 Students use algebraic
More informationLecture 10: Reinforcement Learning
Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation
More informationLearning Methods in Multilingual Speech Recognition
Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex
More informationWHEN THERE IS A mismatch between the acoustic
808 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 3, MAY 2006 Optimization of Temporal Filters for Constructing Robust Features in Speech Recognition Jeih-Weih Hung, Member,
More informationChallenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley
Challenges in Deep Reinforcement Learning Sergey Levine UC Berkeley Discuss some recent work in deep reinforcement learning Present a few major challenges Show some of our recent work toward tackling
More informationSchool of Innovative Technologies and Engineering
School of Innovative Technologies and Engineering Department of Applied Mathematical Sciences Proficiency Course in MATLAB COURSE DOCUMENT VERSION 1.0 PCMv1.0 July 2012 University of Technology, Mauritius
More informationarxiv: v2 [cs.cv] 30 Mar 2017
Domain Adaptation for Visual Applications: A Comprehensive Survey Gabriela Csurka arxiv:1702.05374v2 [cs.cv] 30 Mar 2017 Abstract The aim of this paper 1 is to give an overview of domain adaptation and
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center
More informationHuman Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
More informationOCR for Arabic using SIFT Descriptors With Online Failure Prediction
OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,
More informationKnowledge Elicitation Tool Classification. Janet E. Burge. Artificial Intelligence Research Group. Worcester Polytechnic Institute
Page 1 of 28 Knowledge Elicitation Tool Classification Janet E. Burge Artificial Intelligence Research Group Worcester Polytechnic Institute Knowledge Elicitation Methods * KE Methods by Interaction Type
More informationAnalysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 2, Ver.1 (Mar - Apr.2015), PP 55-61 www.iosrjournals.org Analysis of Emotion
More informationApplications of data mining algorithms to analysis of medical data
Master Thesis Software Engineering Thesis no: MSE-2007:20 August 2007 Applications of data mining algorithms to analysis of medical data Dariusz Matyja School of Engineering Blekinge Institute of Technology
More informationOn-the-Fly Customization of Automated Essay Scoring
Research Report On-the-Fly Customization of Automated Essay Scoring Yigal Attali Research & Development December 2007 RR-07-42 On-the-Fly Customization of Automated Essay Scoring Yigal Attali ETS, Princeton,
More informationIntroduction to Simulation
Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /
More informationAttributed Social Network Embedding
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, MAY 2017 1 Attributed Social Network Embedding arxiv:1705.04969v1 [cs.si] 14 May 2017 Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua Abstract Embedding
More informationClass-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification
Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Tomi Kinnunen and Ismo Kärkkäinen University of Joensuu, Department of Computer Science, P.O. Box 111, 80101 JOENSUU,
More informationhave to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,
A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994
More informationBusiness Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence
Business Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence COURSE DESCRIPTION This course presents computing tools and concepts for all stages
More informationLearning to Schedule Straight-Line Code
Learning to Schedule Straight-Line Code Eliot Moss, Paul Utgoff, John Cavazos Doina Precup, Darko Stefanović Dept. of Comp. Sci., Univ. of Mass. Amherst, MA 01003 Carla Brodley, David Scheeff Sch. of Elec.
More informationCHAPTER 4: REIMBURSEMENT STRATEGIES 24
CHAPTER 4: REIMBURSEMENT STRATEGIES 24 INTRODUCTION Once state level policymakers have decided to implement and pay for CSR, one issue they face is simply how to calculate the reimbursements to districts
More informationLaboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning
More informationMachine Learning from Garden Path Sentences: The Application of Computational Linguistics
Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,
More informationCS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus
CS 1103 Computer Science I Honors Fall 2016 Instructor Muller Syllabus Welcome to CS1103. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts
More informationISFA2008U_120 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM
Proceedings of 28 ISFA 28 International Symposium on Flexible Automation Atlanta, GA, USA June 23-26, 28 ISFA28U_12 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM Amit Gil, Helman Stern, Yael Edan, and
More informationThe Strong Minimalist Thesis and Bounded Optimality
The Strong Minimalist Thesis and Bounded Optimality DRAFT-IN-PROGRESS; SEND COMMENTS TO RICKL@UMICH.EDU Richard L. Lewis Department of Psychology University of Michigan 27 March 2010 1 Purpose of this
More informationA Neural Network GUI Tested on Text-To-Phoneme Mapping
A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis
More informationDiscriminative Learning of Beam-Search Heuristics for Planning
Discriminative Learning of Beam-Search Heuristics for Planning Yuehua Xu School of EECS Oregon State University Corvallis,OR 97331 xuyu@eecs.oregonstate.edu Alan Fern School of EECS Oregon State University
More informationEvolutive Neural Net Fuzzy Filtering: Basic Description
Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:
More informationMining Association Rules in Student s Assessment Data
www.ijcsi.org 211 Mining Association Rules in Student s Assessment Data Dr. Varun Kumar 1, Anupama Chadha 2 1 Department of Computer Science and Engineering, MVN University Palwal, Haryana, India 2 Anupama
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF
Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download
More informationRicopili: Postimputation Module. WCPG Education Day Stephan Ripke / Raymond Walters Toronto, October 2015
Ricopili: Postimputation Module WCPG Education Day Stephan Ripke / Raymond Walters Toronto, October 2015 Ricopili Overview Ricopili Overview postimputation, 12 steps 1) Association analysis 2) Meta analysis
More informationIssues in the Mining of Heart Failure Datasets
International Journal of Automation and Computing 11(2), April 2014, 162-179 DOI: 10.1007/s11633-014-0778-5 Issues in the Mining of Heart Failure Datasets Nongnuch Poolsawad 1 Lisa Moore 1 Chandrasekhar
More informationFragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing
Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing D. Indhumathi Research Scholar Department of Information Technology
More informationLearning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for
Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email Marilyn A. Walker Jeanne C. Fromer Shrikanth Narayanan walker@research.att.com jeannie@ai.mit.edu shri@research.att.com
More informationInstructor: Mario D. Garrett, Ph.D. Phone: Office: Hepner Hall (HH) 100
San Diego State University School of Social Work 610 COMPUTER APPLICATIONS FOR SOCIAL WORK PRACTICE Statistical Package for the Social Sciences Office: Hepner Hall (HH) 100 Instructor: Mario D. Garrett,
More informationNotes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1
Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial
More informationAUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS
AUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS R.Barco 1, R.Guerrero 2, G.Hylander 2, L.Nielsen 3, M.Partanen 2, S.Patel 4 1 Dpt. Ingeniería de Comunicaciones. Universidad de Málaga.
More informationThe stages of event extraction
The stages of event extraction David Ahn Intelligent Systems Lab Amsterdam University of Amsterdam ahn@science.uva.nl Abstract Event detection and recognition is a complex task consisting of multiple sub-tasks
More informationLearning to Rank with Selection Bias in Personal Search
Learning to Rank with Selection Bias in Personal Search Xuanhui Wang, Michael Bendersky, Donald Metzler, Marc Najork Google Inc. Mountain View, CA 94043 {xuanhui, bemike, metzler, najork}@google.com ABSTRACT
More informationMeasurement. When Smaller Is Better. Activity:
Measurement Activity: TEKS: When Smaller Is Better (6.8) Measurement. The student solves application problems involving estimation and measurement of length, area, time, temperature, volume, weight, and
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationMathematics subject curriculum
Mathematics subject curriculum Dette er ei omsetjing av den fastsette læreplanteksten. Læreplanen er fastsett på Nynorsk Established as a Regulation by the Ministry of Education and Research on 24 June
More information