IM S5028. Customer Analytics. Supervised vs unsupervised techniques. Data Mining techniques

Size: px
Start display at page:

Download "IM S5028. Customer Analytics. Supervised vs unsupervised techniques. Data Mining techniques"

Transcription

1 Customer Analytics Data Mining Techniques and applications to CRM: decision trees and neural networks Data Mining techniques Data mining, or knowledge discovery, is the process of discovering valid, novel and useful patterns in large data sets Many different data mining algorithms exist Statistics Decision trees Neural networks Clustering algorithms Rule induction algorithms Rough sets Genetic algorithms decision trees and neural networks are widely used, they are part of most data mining tools 2 Supervised vs unsupervised techniques Supervised learning techniques - guided by known output(decision trees, most neural network types) Or Unsupervised learning techniques use inputs only Clustering algorithms Kohonen Feature Maps (type of neural networks) They us use similarity measures 3 1

2 Decision Trees Tree-shaped structures Can be converted to rules 4 Decision Trees Decision trees are built by iterative process of splitting the data into partitions Many different algorithms Most common are: ID3, C4.5, CART, CHAID Algorithms differ in the number of splits and the diversity function: Gini index, Entropy 5 Decision trees example (adapted from Information Discovery.Inc,1996) Develop a tree to predict profit Manufact urer State City Smith CA Product Color Profit Los Angeles Blue High Smith AZ Flagstaff Green Low Adams NY NYC Blue High Adams AZ Flagstaff Red Low Johnson NY NYC Green Avg. Johnson CA Los Angeles Red Avg. 6 2

3 First step: the tree algorithm splits the original table into three tables using State attribute Table 1A : For State = AZ Manufacture r Smith Adams These two tables must be split further Stat City e Flagstaf AZ fflagstaf AZ Red Table 1B f : For State = CA Product Color Green Manufacture Stat City r e Smith CA Los Angeles Johnson CA Los Table 1C : For State Angeles = NY Profi t Low Low Product Color Blue Red Manufacture Stat Product Profi City r e Color t Adams NY NYC Blue High Johnson NY NYC Green Avg. This one is classified Profi t High Avg. A decision tree derived from this table 8 Corresponding rules This decision tree above can in fact be translated into a set of rules as follows: 1. IF State= AZ THEN Profit = Low; 2. IF State= CA and Manufacturer = Smith THEN Profit = High; 3. IF State= CA and Manufacturer = Johnson THEN Profit = Avg; 4. IF State= NY and Manufacturer = Adams THEN Profit = High; 5. IF State= NY and Manufacturer = Johnson THEN Profit = Avg; 9 3

4 Tree 2 10 Exercise Note: different trees are possible 1. Derive a different tree using manufacturer attribute first and then state 2. Derive a tree starting with colour attribute Represent the trees as rules Compare the rules 11 Classification And Regression Trees (CART) algorithm Based on binary recursive partitioning looks at all possible splits for all variables searches through them all ranks order each splitting rule on the basis of a quality-of-split criterion measured by a diversity function eg. Gini rule or entropy how well the splitting rule separates the classes contained in the parent node 12 4

5 Entropy Entropy is a measure of disorder If an object can be classified into n classes (Ci,... Cn) and the probability of an object belonging to class Ci is p(ci) the entropy of classification is 13 CART: Classification And Regression Trees once a best split is found, the search is repeated for each child node, until further splitting is impossible or some other criterion is met The tree is then tested on a testing sample(prediction/classification rate, error rate) 14 Decision trees, applications Decision trees can be used for: Prediction (eg. churn prediction) classification, (eg. into, good and bad accounts) Exploration Segmentation 15 5

6 Decision trees can be used for segmentation All Customers Young Old Income high Income low Segment1 Segment2 Segment3 (Zikmund et al 2003) 16 Decision trees can be used for Churn modelling 50 Churners 50 Non-churners New technology? ne old w 30 Churners 20 Churners 50 Non-churners 0 Non-churners Years as customer <= 2.3 years > 2.3 years 25 Churners 10 Non-churners 5 Churners 40 Non-churners Age <= 45 > Churners 0 Non-churners 5 Churners 10 Non-churners (adapted from Berson et al, 2000) 17 Case study: data analysis for marketing Groth resources<ftp://ftp.prenhall.com/pub/ptr/ c++_programming.w-050/groth/> case study

7 Data mining exercises: building models using decision tress tutorial in building models using decision trees Students are to complete the decision tree tutorial from the Groth text, p : Tool : KnowledgeSEEKER download and install (next week tutorial) from <ftp://ftp.prenhall.com/pub/ptr/c++_programmi ng.w-050/groth/> 19 References Groth R. (2000) Data Mining Also: Rules are Much More than Decision Trees, Information Discovery Inc. Salford Systems White Paper Series, An Overview of CART Methodology Berson A.., Smith S., Thearling K (2000), Building Data Mining Applications for CRM, McGrow-Hill 20 7

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

Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees

Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Mariusz Łapczy ski 1 and Bartłomiej Jefma ski 2 1 The Chair of Market Analysis and Marketing Research,

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

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

(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

ABSTRACT. A major goal of human genetics is the discovery and validation of genetic polymorphisms

ABSTRACT. A major goal of human genetics is the discovery and validation of genetic polymorphisms ABSTRACT DEODHAR, SUSHAMNA DEODHAR. Using Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions in Genetic Epidemiology. (Under the direction of Dr. Alison Motsinger-Reif.) A major

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

Lecture 1: Basic Concepts of Machine Learning

Lecture 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 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

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

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Ajith Abraham School of Business Systems, Monash University, Clayton, Victoria 3800, Australia. Email: ajith.abraham@ieee.org

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

Computerized Adaptive Psychological Testing A Personalisation Perspective

Computerized Adaptive Psychological Testing A Personalisation Perspective Psychology and the internet: An European Perspective Computerized Adaptive Psychological Testing A Personalisation Perspective Mykola Pechenizkiy mpechen@cc.jyu.fi Introduction Mixed Model of IRT and ES

More information

Applications of data mining algorithms to analysis of medical data

Applications 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 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

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

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

SARDNET: A Self-Organizing Feature Map for Sequences

SARDNET: A Self-Organizing Feature Map for Sequences SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu

More information

MOODLE 2.0 GLOSSARY TUTORIALS

MOODLE 2.0 GLOSSARY TUTORIALS BEGINNING TUTORIALS SECTION 1 TUTORIAL OVERVIEW MOODLE 2.0 GLOSSARY TUTORIALS The glossary activity module enables participants to create and maintain a list of definitions, like a dictionary, or to collect

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 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

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

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming Data Mining VI 205 Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming C. Romero, S. Ventura, C. Hervás & P. González Universidad de Córdoba, Campus Universitario de

More information

Student Mobility Rates in Massachusetts Public Schools

Student Mobility Rates in Massachusetts Public Schools Student Mobility Rates in Massachusetts Public Schools Introduction The Massachusetts Department of Elementary and Secondary Education (ESE) calculates and reports mobility rates as part of its overall

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

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

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

arxiv: v1 [cs.cv] 10 May 2017

arxiv: v1 [cs.cv] 10 May 2017 Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University

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

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF

ADVANCED 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 information

Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering

Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering Time and Place: MW 3:00-4:20pm, A126 Wells Hall Instructor: Dr. Marianne Huebner Office: A-432 Wells Hall

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

Courses in English. Application Development Technology. Artificial Intelligence. 2017/18 Spring Semester. Database access

Courses in English. Application Development Technology. Artificial Intelligence. 2017/18 Spring Semester. Database access The courses availability depends on the minimum number of registered students (5). If the course couldn t start, students can still complete it in the form of project work and regular consultations with

More information

Comparison of EM and Two-Step Cluster Method for Mixed Data: An Application

Comparison of EM and Two-Step Cluster Method for Mixed Data: An Application International Journal of Medical Science and Clinical Inventions 4(3): 2768-2773, 2017 DOI:10.18535/ijmsci/ v4i3.8 ICV 2015: 52.82 e-issn: 2348-991X, p-issn: 2454-9576 2017, IJMSCI Research Article Comparison

More information

The Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence Algorithms

The Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence Algorithms IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS The Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence

More information

Welcome to. ECML/PKDD 2004 Community meeting

Welcome to. ECML/PKDD 2004 Community meeting Welcome to ECML/PKDD 2004 Community meeting A brief report from the program chairs Jean-Francois Boulicaut, INSA-Lyon, France Floriana Esposito, University of Bari, Italy Fosca Giannotti, ISTI-CNR, Pisa,

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

CS4491/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 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 information

Business 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 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 information

Mining Association Rules in Student s Assessment Data

Mining 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 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

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

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

Grade 2: Using a Number Line to Order and Compare Numbers Place Value Horizontal Content Strand

Grade 2: Using a Number Line to Order and Compare Numbers Place Value Horizontal Content Strand Grade 2: Using a Number Line to Order and Compare Numbers Place Value Horizontal Content Strand Texas Essential Knowledge and Skills (TEKS): (2.1) Number, operation, and quantitative reasoning. The student

More information

Ricopili: 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: 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 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

Human Emotion Recognition From Speech

Human 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 information

Trends in College Pricing

Trends in College Pricing Trends in College Pricing 2009 T R E N D S I N H I G H E R E D U C A T I O N S E R I E S T R E N D S I N H I G H E R E D U C A T I O N S E R I E S Highlights Published Tuition and Fee and Room and Board

More information

Issues in the Mining of Heart Failure Datasets

Issues 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 information

CNS 18 21th Communications and Networking Simulation Symposium

CNS 18 21th Communications and Networking Simulation Symposium CNS 18 21th Communications and Networking Simulation Symposium Spring Simulation Multi-conference 2018 Organizing Committee AAA General Chair: Dr. Abdolreza Abhari, aabhari@ryerson.ca Ryerson University,

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

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

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio 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 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

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

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

Welcome. Paulo Goes Dean, Eller College of Management Welcome Our region

Welcome. Paulo Goes Dean, Eller College of Management Welcome Our region Welcome. Paulo Goes Dean, Welcome. Our region Outlook for Tucson Patricia Feeney Executive Director, Southern Arizona Market Chase George W. Hammond, Ph.D. Director, University of Arizona 1 Visit the award-winning

More information

Learning Methods for Fuzzy Systems

Learning Methods for Fuzzy Systems Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8

More information

Analyzing sentiments in tweets for Tesla Model 3 using SAS Enterprise Miner and SAS Sentiment Analysis Studio

Analyzing sentiments in tweets for Tesla Model 3 using SAS Enterprise Miner and SAS Sentiment Analysis Studio SCSUG Student Symposium 2016 Analyzing sentiments in tweets for Tesla Model 3 using SAS Enterprise Miner and SAS Sentiment Analysis Studio Praneth Guggilla, Tejaswi Jha, Goutam Chakraborty, Oklahoma State

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

Managing Experience for Process Improvement in Manufacturing

Managing Experience for Process Improvement in Manufacturing Managing Experience for Process Improvement in Manufacturing Radhika Selvamani B., Deepak Khemani A.I. & D.B. Lab, Dept. of Computer Science & Engineering I.I.T.Madras, India khemani@iitm.ac.in bradhika@peacock.iitm.ernet.in

More information

Using 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 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 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

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

The taming of the data:

The taming of the data: The taming of the data: Using text mining in building a corpus for diachronic analysis Stefania Degaetano-Ortlieb, Hannah Kermes, Ashraf Khamis, Jörg Knappen, Noam Ordan and Elke Teich Background Big data

More information

University of Groningen. Systemen, planning, netwerken Bosman, Aart

University 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 information

Multimedia Application Effective Support of Education

Multimedia Application Effective Support of Education Multimedia Application Effective Support of Education Eva Milková Faculty of Science, University od Hradec Králové, Hradec Králové, Czech Republic eva.mikova@uhk.cz Abstract Multimedia applications have

More information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A 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 information

Matching Similarity for Keyword-Based Clustering

Matching Similarity for Keyword-Based Clustering Matching Similarity for Keyword-Based Clustering Mohammad Rezaei and Pasi Fränti University of Eastern Finland {rezaei,franti}@cs.uef.fi Abstract. Semantic clustering of objects such as documents, web

More information

ScienceDirect. A Framework for Clustering Cardiac Patient s Records Using Unsupervised Learning Techniques

ScienceDirect. A Framework for Clustering Cardiac Patient s Records Using Unsupervised Learning Techniques Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 98 (2016 ) 368 373 The 6th International Conference on Current and Future Trends of Information and Communication Technologies

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

SECTION 12 E-Learning (CBT) Delivery Module

SECTION 12 E-Learning (CBT) Delivery Module SECTION 12 E-Learning (CBT) Delivery Module Linking a CBT package (file or URL) to an item of Set Training 2 Linking an active Redkite Question Master assessment 2 to the end of a CBT package Removing

More information

Multiple Measures Assessment Project - FAQs

Multiple Measures Assessment Project - FAQs Multiple Measures Assessment Project - FAQs (This is a working document which will be expanded as additional questions arise.) Common Assessment Initiative How is MMAP research related to the Common Assessment

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

An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District

An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District Report Submitted June 20, 2012, to Willis D. Hawley, Ph.D., Special

More information

Naviance Family Connection

Naviance Family Connection What is it? Naviance Family Connection Junior Year Naviance Family Connection is a web-based program that allows you and your parents to organize and manage your college search process. It also allows

More information

FY year and 3-year Cohort Default Rates by State and Level and Control of Institution

FY year and 3-year Cohort Default Rates by State and Level and Control of Institution Student Aid Policy Analysis FY2007 2-year and 3-year Cohort Default Rates by State and Level and Control of Institution Mark Kantrowitz Publisher of FinAid.org and FastWeb.com January 5, 2010 EXECUTIVE

More information

Using a Native Language Reference Grammar as a Language Learning Tool

Using a Native Language Reference Grammar as a Language Learning Tool Using a Native Language Reference Grammar as a Language Learning Tool Stacey I. Oberly University of Arizona & American Indian Language Development Institute Introduction This article is a case study in

More information

Team Formation for Generalized Tasks in Expertise Social Networks

Team Formation for Generalized Tasks in Expertise Social Networks IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust Team Formation for Generalized Tasks in Expertise Social Networks Cheng-Te Li Graduate

More information

On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC

On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these

More information

Syllabus Education Department Lincoln University EDU 311 Social Studies Methods

Syllabus Education Department Lincoln University EDU 311 Social Studies Methods Syllabus Education Department Lincoln University EDU 311 Social Studies Methods Instructor: Prof. Kenneth Parker Credits: 3 Room: Time: Office/Phone/Ext: Dickey Hall Room 330/ Extension 7603 E-mail: Kparker@lincoln.edu

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

Grammars & Parsing, Part 1:

Grammars & Parsing, Part 1: Grammars & Parsing, Part 1: Rules, representations, and transformations- oh my! Sentence VP The teacher Verb gave the lecture 2015-02-12 CS 562/662: Natural Language Processing Game plan for today: Review

More information

ACCOUNTING FOR MANAGERS BU-5190-AU7 Syllabus

ACCOUNTING FOR MANAGERS BU-5190-AU7 Syllabus HEALTH CARE ADMINISTRATION MBA ACCOUNTING FOR MANAGERS BU-5190-AU7 Syllabus Winter 2010 P LYMOUTH S TATE U NIVERSITY, C OLLEGE OF B USINESS A DMINISTRATION 1 Page 2 PLYMOUTH STATE UNIVERSITY College of

More information

BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING

BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING Gábor Gosztolya 1, Tamás Grósz 1, László Tóth 1, David Imseng 2 1 MTA-SZTE Research Group on Artificial

More information

learning collegiate assessment]

learning collegiate assessment] [ collegiate learning assessment] INSTITUTIONAL REPORT 2005 2006 Kalamazoo College council for aid to education 215 lexington avenue floor 21 new york new york 10016-6023 p 212.217.0700 f 212.661.9766

More information

Higher Education Six-Year Plans

Higher Education Six-Year Plans Higher Education Six-Year Plans 2018-2024 House Appropriations Committee Retreat November 15, 2017 Tony Maggio, Staff Background The Higher Education Opportunity Act of 2011 included the requirement for

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

INTERMEDIATE ALGEBRA PRODUCT GUIDE

INTERMEDIATE ALGEBRA PRODUCT GUIDE Welcome Thank you for choosing Intermediate Algebra. This adaptive digital curriculum provides students with instruction and practice in advanced algebraic concepts, including rational, radical, and logarithmic

More information

Improved Effects of Word-Retrieval Treatments Subsequent to Addition of the Orthographic Form

Improved Effects of Word-Retrieval Treatments Subsequent to Addition of the Orthographic Form Orthographic Form 1 Improved Effects of Word-Retrieval Treatments Subsequent to Addition of the Orthographic Form The development and testing of word-retrieval treatments for aphasia has generally focused

More information

Preference Learning in Recommender Systems

Preference Learning in Recommender Systems Preference Learning in Recommender Systems Marco de Gemmis, Leo Iaquinta, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro Department of Computer Science University of Bari Aldo

More information

STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point.

STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. STT 231 Test 1 Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. 1. A professor has kept records on grades that students have earned in his class. If he

More information

K-Medoid Algorithm in Clustering Student Scholarship Applicants

K-Medoid Algorithm in Clustering Student Scholarship Applicants Scientific Journal of Informatics Vol. 4, No. 1, May 2017 p-issn 2407-7658 http://journal.unnes.ac.id/nju/index.php/sji e-issn 2460-0040 K-Medoid Algorithm in Clustering Student Scholarship Applicants

More information

university of wisconsin MILWAUKEE Master Plan Report

university of wisconsin MILWAUKEE Master Plan Report university of wisconsin MILWAUKEE Master Plan Report 2010 introduction CUNNINGHAM 18 INTRODUCTION EMS CHEMISTRY LAPHAM 19 INTRODCUCTION introduction The University of Wisconsin-Milwaukee (UWM) is continually

More information

Electromagnetic Spectrum Webquest Answer Key

Electromagnetic Spectrum Webquest Answer Key Webquest Answer Key Free PDF ebook Download: Webquest Answer Key Download or Read Online ebook electromagnetic spectrum webquest answer key in PDF Format From Best User Guide Database Section:. & Light

More information

CS 446: Machine Learning

CS 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 information

AQUA: An Ontology-Driven Question Answering System

AQUA: An Ontology-Driven Question Answering System AQUA: An Ontology-Driven Question Answering System Maria Vargas-Vera, Enrico Motta and John Domingue Knowledge Media Institute (KMI) The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom.

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

Improving Simple Bayes. Abstract. The simple Bayesian classier (SBC), sometimes called

Improving Simple Bayes. Abstract. The simple Bayesian classier (SBC), sometimes called Improving Simple Bayes Ron Kohavi Barry Becker Dan Sommereld Data Mining and Visualization Group Silicon Graphics, Inc. 2011 N. Shoreline Blvd. Mountain View, CA 94043 fbecker,ronnyk,sommdag@engr.sgi.com

More information

Decision Analysis. Decision-Making Problem. Decision Analysis. Part 1 Decision Analysis and Decision Tables. Decision Analysis, Part 1

Decision Analysis. Decision-Making Problem. Decision Analysis. Part 1 Decision Analysis and Decision Tables. Decision Analysis, Part 1 Decision Support: Decision Analysis Jožef Stefan International Postgraduate School, Ljubljana Programme: Information and Communication Technologies [ICT3] Course Web Page: http://kt.ijs.si/markobohanec/ds/ds.html

More information