Speed dating Classification What you should know about dating Stephen Cohen Rajesh Ranganath Te Thamrongrattanarit.

Slides:

Advertisements

Similar presentations

Pseudo-Relevance Feedback For Multimedia Retrieval By Rong Yan, Alexander G. and Rong Jin Mwangi S. Kariuki

Advertisements

Machine Learning Homework

Relevant characteristics extraction from semantically unstructured data PhD title : Data mining in unstructured data Daniel I. MORARIU, MSc PhD Supervisor:

Supervised Learning Techniques over Twitter Data Kleisarchaki Sofia.

Support vector machine

Supervised Learning Recap

Vineel Pratap Girish Govind Abhilash Veeragouni. Human listeners are capable of extracting information from the acoustic signal beyond just the linguistic.

Face Recognition & Biometric Systems Support Vector Machines (part 2)

AN IMPROVED AUDIO Jenn Tam Computer Science Dept. Carnegie Mellon University SOAPS 2008, Pittsburgh, PA.

Object Recognition with Informative Features and Linear Classification Authors: Vidal-Naquet & Ullman Presenter: David Bradley.

Extracting Social Meaning Identifying Interactional Style in Spoken Conversation Jurafsky et al ‘09 Presented by Laura Willson.

CSE 300: Software Reliability Engineering Topics covered: Software metrics and software reliability Software complexity and software quality.

1 Ensembles of Nearest Neighbor Forecasts Dragomir Yankov, Eamonn Keogh Dept. of Computer Science & Eng. University of California Riverside Dennis DeCoste.

Forecasting with Twitter data Presented by : Thusitha Chandrapala MARTA ARIAS, ARGIMIRO ARRATIA, and RAMON XURIGUERA.

CSC 478 Programming Data Mining Applications Course Summary Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.

Career Opportunities for Linguists in Industry Vita Markman Computational Linguistics Engineer DIMG.

Machine Learning Queens College Lecture 1: Introduction.

Machine Learning Queens College Lecture 13: SVM Again.

Project 1 : Eigen-Faces Applied to Speech Style Classification Brad Keserich, Senior, Computer Engineering College of Engineering and Applied Science;

Outline Separating Hyperplanes – Separable Case

Data Mining Joyeeta Dutta-Moscato July 10, Wherever we have large amounts of data, we have the need for building systems capable of learning information.

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Predicting Solar Generation from Weather Forecasts Using Machine Learning Navin.

Interactive Dialogue Systems Professor Diane Litman Computer Science Department & Learning Research and Development Center University of Pittsburgh Pittsburgh,

A study on Prediction on Listener Emotion in Speech for Medical Doctor Interface M.Kurematsu Faculty of Software and Information Science Iwate Prefectural.

CS460/626 : Natural Language Processing/Speech, NLP and the Web (Lecture 16– Linear and Logistic Regression) Pushpak Bhattacharyya CSE Dept., IIT Bombay.

1 SUPPORT VECTOR MACHINES İsmail GÜNEŞ. 2 What is SVM? A new generation learning system. A new generation learning system. Based on recent advances in.

Learning Theory Reza Shadmehr logistic regression, iterative re-weighted least squares.

This work is supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number.

Use of Machine Learning in Chemoinformatics Irene Kouskoumvekaki Associate Professor December 12th, 2012 Biological Sequence Analysis course.

Applied Linguistics Nick Campbell & Hideki Kashioka learning how people speak.

Overview of Part I, CMSC5707 Advanced Topics in Artificial Intelligence KH Wong (6 weeks) Audio signal processing – Signals in time & frequency domains.

CS Statistical Machine learning Lecture 18 Yuan (Alan) Qi Purdue CS Oct

Jun-Won Suh Intelligent Electronic Systems Human and Systems Engineering Department of Electrical and Computer Engineering Speaker Verification System.

A Comparative Study of Kernel Methods for Classification Applications Yan Liu Oct 21, 2003.

Text Classification 2 David Kauchak cs459 Fall 2012 adapted from:

Spam Detection Ethan Grefe December 13, 2013.

Indirect Supervision Protocols for Learning in Natural Language Processing II. Learning by Inventing Binary Labels This work is supported by DARPA funding.

October 2005CSA3180 NLP1 CSA3180 Natural Language Processing Introduction and Course Overview.

Classification Derek Hoiem CS 598, Spring 2009 Jan 27, 2009.

Principal Component Analysis Machine Learning. Last Time Expectation Maximization in Graphical Models – Baum Welch.

DATA MINING WITH CLUSTERING AND CLASSIFICATION Spring 2007, SJSU Benjamin Lam.

Topic Modeling using Latent Dirichlet Allocation

Carolyn Penstein Rosé Language Technologies Institute Human-Computer Interaction Institute School of Computer Science With funding from the National Science.

Hello, Who is Calling? Can Words Reveal the Social Nature of Conversations?

Data Mining Practical Machine Learning Tools and Techniques By I. H. Witten, E. Frank and M. A. Hall DM Finals Study Guide Rodney Nielsen.

Computational Intelligence: Methods and Applications Lecture 29 Approximation theory, RBF and SFN networks Włodzisław Duch Dept. of Informatics, UMK Google:

CSE 5331/7331 F'07© Prentice Hall1 CSE 5331/7331 Fall 2007 Regression Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist.

CS Statistical Machine learning Lecture 12 Yuan (Alan) Qi Purdue CS Oct

Final Report (30% final score) Bin Liu, PhD, Associate Professor.

CSC 478 Programming Data Mining Applications Course Summary Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.

Support Vector Machines Optimization objective Machine Learning.

Concept-Based Analysis of Scientific Literature Chen-Tse Tsai, Gourab Kundu, Dan Roth UIUC.

Research Methodology Proposal Prepared by: Norhasmizawati Ibrahim (813750)

Unveiling Zeus Automated Classification of Malware Samples Abedelaziz Mohaisen Omar Alrawi Verisign Inc, VA, USA Verisign Labs, VA, USA

Linear Classifiers (LC) J.-S. Roger Jang ( 張智星 ) MIR Lab, CSIE Dept. National Taiwan University.

Does one size really fit all? Evaluating classifiers in a Bag-of-Visual-Words classification Christian Hentschel, Harald Sack Hasso Plattner Institute.

Recommendation in Scholarly Big Data

The Next Frontier in TAR: Choose Your Own Algorithm

Table 1. Advantages and Disadvantages of Traditional DM/ML Methods

For Evaluating Dialog Error Conditions Based on Acoustic Information

Special Topics in Data Mining Applications Focus on: Text Mining

LINEAR AND NON-LINEAR CLASSIFICATION USING SVM and KERNELS

Machine Learning Week 1.

CS 2750: Machine Learning Support Vector Machines

Mathematical Foundations of BME Reza Shadmehr

Recitation 6: Kernel SVM

Support Vector Machine I

Introduction to Sentiment Analysis

Support Vector Machines 2

CS249: Neural Language Model

Presentation transcript:

Speed dating Classification What you should know about dating Stephen Cohen Rajesh Ranganath Te Thamrongrattanarit

Speed dating A rabbi invented speed dating 10 years ago Heres how it works… Goal : To find the model that predicts men and womens decisions

Massive feature extraction Easy things Word count Count of certain words Backchannelling Post-conversation word count Question count Non-academic discussion Etc. Difficult things Latent Dirichlet Allocation Latent Semantic Analysis Various vector similarity metrics Speed of conversation Etc.

Classifiers and other techniques Lexical Feature Extraction Logistic Regression with linear kernel Support Vector Machines with… Linear kernel RBF kernel

Evaluation Principle Component Analysis For every feature we add, we capture more variance. = good sign The Rajesh Metric for evaluating models Logistic Regression and SVM work just as well. Pick the best model based on the Rajesh Metric Analyze regression coefficients of the best model

What you should know about dating Men are more likely to say yes if.. More positive words are uttered. [lexical features] Men and women talk about the same topics [Latent Dirichlet Allocation and Jenson-Shannon similarity] Men:women word count ratio is high Women ask more questions! [count of question marks] but opposite effect on women And more… Womens decisions can hardly be predicted by the model. (Women are hard to understand…) Women are more likely to say yes if they talk about the past. Physical appearance? Voice? Speech? Chemistry?

Acknowledgement Professor Dan Jurafsky (Linguistics Dept.) Professor Dan McFarland (School of Education) Stephan Stiller (Computer Science) David Hall (Symbolic Systems and CS)