Trees: Themes and Variations

Size: px
Start display at page:

Download "Trees: Themes and Variations"

Transcription

1 Trees: Themes and Variations Prof. Mari Ostendorf Outline Preface Decision Trees Bagging Boosting BoosTexter 1

2 Preface: Vector Classifiers Today we again deal with vector classifiers and supervised training: Given a labeled training set {(x i, c i )} with vector observations, learn a classifier ĉ = F (x). (In the remainder of the notes, I ll omit boldface for vectors to simplify things, but everything will be a vector.) Classification issues and concepts that we ll touch on: Multiclass problems: Some classifiers are good for > 2 classes; others are developed for binary decisions and need special tricks for multiclass problems. Features playing a key role, including: complicating the classifier, feature selection, feature analysis Direct probabilistic model: On Tuesday we used p(x c)p(c); today we will learn p(c x) directly. Bias and variance: In statistical learning, models (or classifiers) are learned from a random sample of data (the training set). Because the data is random, the resulting classifier is random, i.e. can give slightly different answers if trained on a different data set. A high variance classifier is one that is very sensitive to the training sample (not a good thing). Class distribution skew: when you have a lot more data from one class than another, and when all errors are treated equally, classifiers tend to put their effort into the more popular classes. This makes sense from a min error perspective, but sometimes it makes it hard to learn to predict rare events. 2

3 Decision Trees A decision tree is an ordered (tree-structured) sequence of questions that are asked about the features x i in the vector x. The feature vector passes from the root of the tree to a specific leaf based on the answers to the each successive question. Questions correspond to nodes of the tree, and the leaf nodes (terminal nodes) are associated with the classifier decision and/or the predicted class posterior p(c T (x)) where T ( ) is the tree. Typically questions are binary. They may take many forms and handle different types of features, e.g. is x i > T? (for numeric features) is x i = green? (for categorical features, attribute-value questions) is x i A? (for categorical features, set membership) Decision trees are one of the most popular methods of machine learning, in part because: They easily handle multiclass problems. They easily handle heterogeneous features (categorical and numeric) without requiring independence assumptions. They take care of feature selection automatically (x i only asked about if it is useful), as well as account for the relative importance of features (fewer questions about less important features). The sequence of questions learned is easy to interpret, so trees can be used for data analysis. They allow you to combine knowledge engineering (question design) and ignorance modeling (statistical learning). 3

4 There are two main steps: Tree growing Learning a Decision Tree Tree pruning (determining the right size) Tree growing is based on a greedy algorithm for improving some objective function, such as minimum entropy of p(c T (x)) (which is the same as maximum mutual information) or minimum error rate. For each leaf node t in the current tree For each possible question q For each possible parameter a of the question compute the objective function gain G(t, q, a) Find the best parameter for q and t: a = argmax a G(t, q, a) Find the best question for t: q = argmax q G(t, q, a ) Find the best node to split: t = argmax t G(t, q, a ) Split that node and repeat. (Note: you can save the q and a information so that you don t need to redo all the tests.) The greedy approach is used because the optimal search is way too slow. However, since it is greedy, it is often better to use other objective functions besides minimum error rate. Like any learning problem, if you learn a model with too many parameters relative to the amount of data (overtraining), then the model won t generalize very well to new samples. It is easy to overtrain decision trees, so you need a mechanism to pick the right size. 4

5 Important concepts: Learning the Right Sized Tree It is better to prune back a big tree than to stop the growing process, since big gains can follow small gains. Consider the 2-class problem with 2 modes per class as in: A B B A The first split doesn t change the predictions, but the second split allows you to predict the classes perfectly. You need to use different data for growing vs. pruning. If you have a lot of data, just use a held out set. If you don t have a lot of data, use cross-validation. Cross-validation pruning: Partition the training data into N sets. Rotate through each set, training on all but the i-th set and pruning with that set. Find the cost/complexity trade-off for each case, and average to come up with the optimal pruning point. Then retrain a tree on the full data set, and prune according to this cost/complexity criterion (loss in G relative to number of nodes pruned). Most decision tree software takes care of this for you, but you need to remember to enable pruning. 5

6 Knowledge Engineering and Tree Design A good sequence of questions is learned automatically from data, but the set of possible questions can be improved by a human. Questions that software packages can think of: if you specify that the feature is numeric: is x i > T? if you specify that the feature is categorical (including binary): is x i = a? (attribute-value questions) is x i A? (set membership with A learned automatically, only in some toolkits and only when the possible values of x i is small, e.g. < 10) In theory, the decision tree learns complex questions through the sequence it asks (set membership, combinations of variables), BUT in practice, limited data impedes learning. Answer: knowledge engineering. Set membership: the human designer incorporates questions (or features, depending on software) that are flags for different sets that might be useful. Design simple combinations of categorical features by hand. Outside of tree design, learn a good linear transformation (x = w t x) of a subvector of continuous variables using principal component analysis (PCA) or linear discriminant analysis (LDA). Use new feature x and let tree design learn threshold. [covered next week] The decision tree will pick, so err on the side of too many of such groups and feature combinations instead of too few. 6

7 Interpreting Decision Trees Decision trees have the advantage that they are easy to interpret. The most important prediction variables are the first questions in the tree (near the root node). Variables that are associated with questions in many places in the tree are usually important (though this can also be a reflection of the need for complex questions). Some decision tree software provides output that scores variable for their importance based on information gain associated with each question in training. BUT, because of the complex interactions and instability of tree design, feature analysis and selection often benefits from further analysis, e.g. Design trees with individual features (or subgroups of features) how much information does this feature give on its own? Design trees leaving out one feature at a time (or subgroups of features) how much does this feature give in combination with other features? 7

8 Limitations of Decision Trees Decision trees divide up the training data with each question that is learned, which is good when there are dependencies but not good for variables that are independent. (This also motivates use of complex questions.) Subsequent decisions are based on less data, so may be less reliable. Feature selection is not perfect. If samples from an infrequent class get split up, it may be impossible to learn questions that predict that class. Decision trees are high variance (not stable) a change in the data sample could cause a very different tree to be learned. This is particularly a problem when there is not a lot of training data. Decision trees can have trouble learning to predict infrequent classes. So what do we do if we like the positive aspects of decision trees? Downsampling the more frequent classes to learn p(c T (x)), then compensate for change (so as to correctly weight the more frequent class) by: p(c T (x)) p(c T (x))p 0 (c) where p 0 (c) is the empirical (skewed) class prior. Bagging (to deal with instability and underutilization of data in downsampling) Boosting (another way to deal with skew) 8

9 Bagging Bagging is a general approach for designing lower variance classifiers, but it is especially popular for decision trees. Repeat for i = 1,..., N: Randomly sample (with replacement) from the training data to create a smaller training sample i. Train tree T i on this data sample, providing p(c T i (x)) Apply all N classifiers to a test sample and average the class posteriors: p(c x) = 1 N Then make a decision according to N i=1 p(c T i (x)) c = argmax c p(c x) Typically, each individual sampled training subset i would be about 70% of the size of the full sample, and N would be fairly large, chosen based on a development set. If you resample to balance class distribution, then the sampled subset would be smaller, and you would probably want a larger N. Since bigger N is more costly in terms of both memory and compute, you don t want it bigger than it needs to be for good performance. Does bagging always help? Not necessarily. The approach trades off increased model error associated with having a smaller training set with reduced variance due to averaging. For stable classifiers, bagging often isn t worth the added cost. 9

10 AdaBoost Like bagging, boosting is a general method for improving the accuracy of a given learning algorithm. It is similar to bagging in that you combine a bunch of classifiers, but the classifiers are designed by reweighting (rather than resampling) the data. A practical boosting algorithm is AdaBoost: Let D 1 (i) = 1/m be the initial weight of the i-th data sample. For t = 1,..., T Train a weak learner h t (x) using distribution D t (to weight samples or for sampling if learner can t use weighted samples). Get a weak hypothesis with error ɛ t on the training data. Choose α t = 1 2 ln[1 ɛ t ɛ t ]. (Note: we assume that the first learner gives ɛ 1 < 0.5, so α t > 0 for all t.) Update D t+1 (i) by e ±α t according to whether that sample was correctly classified, i.e. increase the weight for incorrectly classified samples and decrease it for correctly classified samples. If decisions h t (x i ) and class labels y i take on values ±1, then the new weight is: D t+1 (i) = 1 Z t D t (i) exp( α t y i h t (x i )) where Z t is a normalization term chosen so that D t+1 will be a valid distribution (sum to 1). The final classifier will be a weighted combination of the weak learners: T t=1 where T is determined empirically. α t h t (x) 10

11 Notes on boosting: AdaBoost works well for 2-class problems, but not always for multiclass problems. If the initial learner is too weak, then you need to implement multiclass decisions as a combination of binary decisions. The theory of boosting aimed at showing how to make weak learners strong, but you can use AdaBoost to make good learners better as well as making weak learners better. AdaBoost tends to be less sensitive to problems of skewed priors, because it boosts up the weight on infrequent classes without dividing the data as for decision trees. 11

12 BoosTexter BoosTexter is AdaBoost specially designed for text classification problems. The weak learner is a single-question decision tree (called a decision stump ), so typically large T is required. This makes BoosTexter very fast and often gives good results, but it may be possible to do better by boosting on top of decision trees. The features can include almost anything (like decision trees), but the software easily incorporates word and word pair features since it is designed for text problems. BoosTexter has been used with success for problems like: Topic classification Sentence segmentation and punctuation prediction Dialog act tagging Sentence extraction for information distillation For more information, see paper by Schapire and Singer, Machine Learning, 39(2/3): ,

Lecture 1: Machine Learning Basics

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

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine 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 information

CS Machine Learning

CS 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 information

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Introduction 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 information

(Sub)Gradient Descent

(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 information

Python Machine Learning

Python 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 information

Switchboard Language Model Improvement with Conversational Data from Gigaword

Switchboard 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 information

Rule Learning With Negation: Issues Regarding Effectiveness

Rule 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 information

Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages

Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Nuanwan Soonthornphisaj 1 and Boonserm Kijsirikul 2 Machine Intelligence and Knowledge Discovery Laboratory Department of Computer

More information

Assignment 1: Predicting Amazon Review Ratings

Assignment 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 information

Chinese Language Parsing with Maximum-Entropy-Inspired Parser

Chinese 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 information

Rule Learning with Negation: Issues Regarding Effectiveness

Rule 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 information

Twitter Sentiment Classification on Sanders Data using Hybrid Approach

Twitter 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 information

Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

More information

Calibration of Confidence Measures in Speech Recognition

Calibration 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 information

WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT

WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT PRACTICAL APPLICATIONS OF RANDOM SAMPLING IN ediscovery By Matthew Verga, J.D. INTRODUCTION Anyone who spends ample time working

More information

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON 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 information

Generative models and adversarial training

Generative 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 information

Model Ensemble for Click Prediction in Bing Search Ads

Model 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 information

MYCIN. The MYCIN Task

MYCIN. The MYCIN Task MYCIN Developed at Stanford University in 1972 Regarded as the first true expert system Assists physicians in the treatment of blood infections Many revisions and extensions over the years The MYCIN Task

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 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 information

Probability and Statistics Curriculum Pacing Guide

Probability and Statistics Curriculum Pacing Guide Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

More information

12- A whirlwind tour of statistics

12- A whirlwind tour of statistics CyLab HT 05-436 / 05-836 / 08-534 / 08-734 / 19-534 / 19-734 Usable Privacy and Security TP :// C DU February 22, 2016 y & Secu rivac rity P le ratory bo La Lujo Bauer, Nicolas Christin, and Abby Marsh

More information

Learning From the Past with Experiment Databases

Learning 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 information

Artificial Neural Networks written examination

Artificial 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 information

Exploration. CS : Deep Reinforcement Learning Sergey Levine

Exploration. 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 information

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best

More information

The 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, / 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 information

Semi-Supervised Face Detection

Semi-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 information

AGS 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 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 information

The stages of event extraction

The 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 information

Universidade do Minho Escola de Engenharia

Universidade do Minho Escola de Engenharia Universidade do Minho Escola de Engenharia Universidade do Minho Escola de Engenharia Dissertação de Mestrado Knowledge Discovery is the nontrivial extraction of implicit, previously unknown, and potentially

More information

Truth Inference in Crowdsourcing: Is the Problem Solved?

Truth Inference in Crowdsourcing: Is the Problem Solved? Truth Inference in Crowdsourcing: Is the Problem Solved? Yudian Zheng, Guoliang Li #, Yuanbing Li #, Caihua Shan, Reynold Cheng # Department of Computer Science, Tsinghua University Department of Computer

More information

The Evolution of Random Phenomena

The Evolution of Random Phenomena The Evolution of Random Phenomena A Look at Markov Chains Glen Wang glenw@uchicago.edu Splash! Chicago: Winter Cascade 2012 Lecture 1: What is Randomness? What is randomness? Can you think of some examples

More information

Learning Methods in Multilingual Speech Recognition

Learning 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 information

A Case Study: News Classification Based on Term Frequency

A 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 information

Notes 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 (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 information

System 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 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 information

OCR for Arabic using SIFT Descriptors With Online Failure Prediction

OCR 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 information

Comment-based Multi-View Clustering of Web 2.0 Items

Comment-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 information

Corrective Feedback and Persistent Learning for Information Extraction

Corrective Feedback and Persistent Learning for Information Extraction Corrective Feedback and Persistent Learning for Information Extraction Aron Culotta a, Trausti Kristjansson b, Andrew McCallum a, Paul Viola c a Dept. of Computer Science, University of Massachusetts,

More information

THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF MATHEMATICS ASSESSING THE EFFECTIVENESS OF MULTIPLE CHOICE MATH TESTS

THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF MATHEMATICS ASSESSING THE EFFECTIVENESS OF MULTIPLE CHOICE MATH TESTS THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF MATHEMATICS ASSESSING THE EFFECTIVENESS OF MULTIPLE CHOICE MATH TESTS ELIZABETH ANNE SOMERS Spring 2011 A thesis submitted in partial

More information

Linking Task: Identifying authors and book titles in verbose queries

Linking 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 information

Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study

Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study Purdue Data Summit 2017 Communication of Big Data Analytics New SAT Predictive Validity Case Study Paul M. Johnson, Ed.D. Associate Vice President for Enrollment Management, Research & Enrollment Information

More information

Axiom 2013 Team Description Paper

Axiom 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 information

Knowledge Transfer in Deep Convolutional Neural Nets

Knowledge 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 information

Discriminative Learning of Beam-Search Heuristics for Planning

Discriminative 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 information

Probabilistic Latent Semantic Analysis

Probabilistic 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 information

INPE São José dos Campos

INPE São José dos Campos INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA

More information

Experiments 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 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 information

An Empirical and Computational Test of Linguistic Relativity

An Empirical and Computational Test of Linguistic Relativity An Empirical and Computational Test of Linguistic Relativity Kathleen M. Eberhard* (eberhard.1@nd.edu) Matthias Scheutz** (mscheutz@cse.nd.edu) Michael Heilman** (mheilman@nd.edu) *Department of Psychology,

More information

Online Updating of Word Representations for Part-of-Speech Tagging

Online Updating of Word Representations for Part-of-Speech Tagging Online Updating of Word Representations for Part-of-Speech Tagging Wenpeng Yin LMU Munich wenpeng@cis.lmu.de Tobias Schnabel Cornell University tbs49@cornell.edu Hinrich Schütze LMU Munich inquiries@cislmu.org

More information

A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and

A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and Planning Overview Motivation for Analyses Analyses and

More information

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick

More information

Large-Scale Web Page Classification. Sathi T Marath. Submitted in partial fulfilment of the requirements. for the degree of Doctor of Philosophy

Large-Scale Web Page Classification. Sathi T Marath. Submitted in partial fulfilment of the requirements. for the degree of Doctor of Philosophy Large-Scale Web Page Classification by Sathi T Marath Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Dalhousie University Halifax, Nova Scotia November 2010

More information

Prediction of Maximal Projection for Semantic Role Labeling

Prediction of Maximal Projection for Semantic Role Labeling Prediction of Maximal Projection for Semantic Role Labeling Weiwei Sun, Zhifang Sui Institute of Computational Linguistics Peking University Beijing, 100871, China {ws, szf}@pku.edu.cn Haifeng Wang Toshiba

More information

The 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 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 information

Algebra 2- Semester 2 Review

Algebra 2- Semester 2 Review Name Block Date Algebra 2- Semester 2 Review Non-Calculator 5.4 1. Consider the function f x 1 x 2. a) Describe the transformation of the graph of y 1 x. b) Identify the asymptotes. c) What is the domain

More information

Multi-Lingual Text Leveling

Multi-Lingual Text Leveling Multi-Lingual Text Leveling Salim Roukos, Jerome Quin, and Todd Ward IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 {roukos,jlquinn,tward}@us.ibm.com Abstract. Determining the language proficiency

More information

Chapter 2 Rule Learning in a Nutshell

Chapter 2 Rule Learning in a Nutshell Chapter 2 Rule Learning in a Nutshell This chapter gives a brief overview of inductive rule learning and may therefore serve as a guide through the rest of the book. Later chapters will expand upon the

More information

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers

More information

WHEN THERE IS A mismatch between the acoustic

WHEN 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 information

4.0 CAPACITY AND UTILIZATION

4.0 CAPACITY AND UTILIZATION 4.0 CAPACITY AND UTILIZATION The capacity of a school building is driven by four main factors: (1) the physical size of the instructional spaces, (2) the class size limits, (3) the schedule of uses, and

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: 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 information

Proof Theory for Syntacticians

Proof Theory for Syntacticians Department of Linguistics Ohio State University Syntax 2 (Linguistics 602.02) January 5, 2012 Logics for Linguistics Many different kinds of logic are directly applicable to formalizing theories in syntax

More information

Softprop: Softmax Neural Network Backpropagation Learning

Softprop: 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 information

Semi-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. 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 information

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method Farhadi F, Sorkhi M, Hashemi S et al. An effective framework for fast expert mining in collaboration networks: A grouporiented and cost-based method. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 27(3): 577

More information

Evolutive Neural Net Fuzzy Filtering: Basic Description

Evolutive 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 information

Defragmenting Textual Data by Leveraging the Syntactic Structure of the English Language

Defragmenting Textual Data by Leveraging the Syntactic Structure of the English Language Defragmenting Textual Data by Leveraging the Syntactic Structure of the English Language Nathaniel Hayes Department of Computer Science Simpson College 701 N. C. St. Indianola, IA, 50125 nate.hayes@my.simpson.edu

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Ch 2 Test Remediation Work Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) High temperatures in a certain

More information

While you are waiting... socrative.com, room number SIMLANG2016

While you are waiting... socrative.com, room number SIMLANG2016 While you are waiting... socrative.com, room number SIMLANG2016 Simulating Language Lecture 4: When will optimal signalling evolve? Simon Kirby simon@ling.ed.ac.uk T H E U N I V E R S I T Y O H F R G E

More information

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Hans Christian 1 ; Mikhael Pramodana Agus 2 ; Derwin Suhartono 3 1,2,3 Computer Science Department,

More information

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus

CS 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 information

CSL465/603 - Machine Learning

CSL465/603 - 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 information

Disambiguation of Thai Personal Name from Online News Articles

Disambiguation of Thai Personal Name from Online News Articles Disambiguation of Thai Personal Name from Online News Articles Phaisarn Sutheebanjard Graduate School of Information Technology Siam University Bangkok, Thailand mr.phaisarn@gmail.com Abstract Since online

More information

Individual Differences & Item Effects: How to test them, & how to test them well

Individual Differences & Item Effects: How to test them, & how to test them well Individual Differences & Item Effects: How to test them, & how to test them well Individual Differences & Item Effects Properties of subjects Cognitive abilities (WM task scores, inhibition) Gender Age

More information

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

Machine 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 information

Data Integration through Clustering and Finding Statistical Relations - Validation of Approach

Data Integration through Clustering and Finding Statistical Relations - Validation of Approach Data Integration through Clustering and Finding Statistical Relations - Validation of Approach Marek Jaszuk, Teresa Mroczek, and Barbara Fryc University of Information Technology and Management, ul. Sucharskiego

More information

How to Judge the Quality of an Objective Classroom Test

How to Judge the Quality of an Objective Classroom Test How to Judge the Quality of an Objective Classroom Test Technical Bulletin #6 Evaluation and Examination Service The University of Iowa (319) 335-0356 HOW TO JUDGE THE QUALITY OF AN OBJECTIVE CLASSROOM

More information

Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition Using Support Vector Machine Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,

More information

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing a Moving Target How Do We Test Machine Learning Systems? Peter Varhol, Technology

More information

Version Space. Term 2012/2013 LSI - FIB. Javier Béjar cbea (LSI - FIB) Version Space Term 2012/ / 18

Version Space. Term 2012/2013 LSI - FIB. Javier Béjar cbea (LSI - FIB) Version Space Term 2012/ / 18 Version Space Javier Béjar cbea LSI - FIB Term 2012/2013 Javier Béjar cbea (LSI - FIB) Version Space Term 2012/2013 1 / 18 Outline 1 Learning logical formulas 2 Version space Introduction Search strategy

More information

Word learning as Bayesian inference

Word learning as Bayesian inference Word learning as Bayesian inference Joshua B. Tenenbaum Department of Psychology Stanford University jbt@psych.stanford.edu Fei Xu Department of Psychology Northeastern University fxu@neu.edu Abstract

More information

Phonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project

Phonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project Phonetic- and Speaker-Discriminant Features for Speaker Recognition by Lara Stoll Research Project Submitted to the Department of Electrical Engineering and Computer Sciences, University of California

More information

Instructor: Mario D. Garrett, Ph.D. Phone: Office: Hepner Hall (HH) 100

Instructor: 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 information

No Parent Left Behind

No Parent Left Behind No Parent Left Behind Navigating the Special Education Universe SUSAN M. BREFACH, Ed.D. Page i Introduction How To Know If This Book Is For You Parents have become so convinced that educators know what

More information

FRAMEWORK FOR IDENTIFYING THE MOST LIKELY SUCCESSFUL UNDERPRIVILEGED TERTIARY STUDY BURSARY APPLICANTS

FRAMEWORK FOR IDENTIFYING THE MOST LIKELY SUCCESSFUL UNDERPRIVILEGED TERTIARY STUDY BURSARY APPLICANTS South African Journal of Industrial Engineering August 2017 Vol 28(2), pp 59-77 FRAMEWORK FOR IDENTIFYING THE MOST LIKELY SUCCESSFUL UNDERPRIVILEGED TERTIARY STUDY BURSARY APPLICANTS R. Steynberg 1 * #,

More information

Detecting English-French Cognates Using Orthographic Edit Distance

Detecting English-French Cognates Using Orthographic Edit Distance Detecting English-French Cognates Using Orthographic Edit Distance Qiongkai Xu 1,2, Albert Chen 1, Chang i 1 1 The Australian National University, College of Engineering and Computer Science 2 National

More information

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models

Learning 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 information

Getting Started with Deliberate Practice

Getting Started with Deliberate Practice Getting Started with Deliberate Practice Most of the implementation guides so far in Learning on Steroids have focused on conceptual skills. Things like being able to form mental images, remembering facts

More information

Mining Student Evolution Using Associative Classification and Clustering

Mining Student Evolution Using Associative Classification and Clustering Mining Student Evolution Using Associative Classification and Clustering 19 Mining Student Evolution Using Associative Classification and Clustering Kifaya S. Qaddoum, Faculty of Information, Technology

More information

A Version Space Approach to Learning Context-free Grammars

A Version Space Approach to Learning Context-free Grammars Machine Learning 2: 39~74, 1987 1987 Kluwer Academic Publishers, Boston - Manufactured in The Netherlands A Version Space Approach to Learning Context-free Grammars KURT VANLEHN (VANLEHN@A.PSY.CMU.EDU)

More information

Beyond the Pipeline: Discrete Optimization in NLP

Beyond the Pipeline: Discrete Optimization in NLP Beyond the Pipeline: Discrete Optimization in NLP Tomasz Marciniak and Michael Strube EML Research ggmbh Schloss-Wolfsbrunnenweg 33 69118 Heidelberg, Germany http://www.eml-research.de/nlp Abstract We

More information

Analysis of Enzyme Kinetic Data

Analysis of Enzyme Kinetic Data Analysis of Enzyme Kinetic Data To Marilú Analysis of Enzyme Kinetic Data ATHEL CORNISH-BOWDEN Directeur de Recherche Émérite, Centre National de la Recherche Scientifique, Marseilles OXFORD UNIVERSITY

More information

On-the-Fly Customization of Automated Essay Scoring

On-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 information

Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany

Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany Jana Kitzmann and Dirk Schiereck, Endowed Chair for Banking and Finance, EUROPEAN BUSINESS SCHOOL, International

More information

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur)

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) 1 Interviews, diary studies Start stats Thursday: Ethics/IRB Tuesday: More stats New homework is available

More information

School Size and the Quality of Teaching and Learning

School Size and the Quality of Teaching and Learning School Size and the Quality of Teaching and Learning An Analysis of Relationships between School Size and Assessments of Factors Related to the Quality of Teaching and Learning in Primary Schools Undertaken

More information