Elkan, Charles - Department of Computer Science and Engineering, University of California at San Diego

• CSE291: Statistical Learning Lecture #13 University of California, San Diego Tuesday, 15 February 2005
• Emotional Expression Recognition using Support Vector Machines
• Learning from Incomplete Data Sameer Agarwal
• Notes on Machine Learning Projects and Charles Elkan
• Supervised Learning with Probabilities Charles Elkan
• Perceptron Classifiers Charles Elkan
• Mixture Models Charles Elkan
• Week 3 Section (10/13/08) by Y. Albert Park Proof of perceptron algorithm
• Clustering Billions of Images with Large Scale Nearest Neighbor Search
• CSE 254 Seminar in learning algorithms Theoretical views of boosting and applications
• Feature selection, L1 vs. L2 regularization, and
• Grouping and Dimensionality Reduction by Locally Linear Embedding
• CSE 254 Seminar in learning algorithms Recognition of Visual Speech Elements
• Shrinkage Techniques for Hierarchical Text Classification*
• Introduction Theoretical Claims Conformal ISOMAP Landmark ISOMAP Summary Global (ISOMAP) versus Local (LLE)
• Automatic Music Annotation Research Exam
• ...there seems something else in life besides time, something which may conveniently called "value", something which is measured not
• Distributed Learning of Lane-Selection Strategies
• Implicit Imitation in Multiagent Reinforcement Learning
• Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
• CSE291: Statistical Learning Lecture #3 University of California, San Diego Tuesday, 11 January 2005
• CSE291: Statistical Learning Lecture #4 University of California, San Diego Tuesday, 13 January 2005
• CSE291: Statistical Learning Lecture #6 University of California, San Diego Tuesday, 25 January 2005
• CSE291: Statistical Learning Lecture #9 University of California, San Diego Tuesday, 1 February 2005
• CSE291: Statistical Learning Lecture #10 University of California, San Diego Tuesday, 3 February 2005
• CSE291: Statistical Learning Lecture #11 University of California, San Diego Tuesday, 8 February 2005
• DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING UNIVERSITY OF CALIFORNIA, SAN DIEGO
• Section 9 Notes, March 5, 2004, by Kristin Branson 1 Learning a Spam Classifier
• Section 2 Notes, January 16, 2004, by Kristin Branson 1 Implement the A
• Discussion 1 notes. January 9th (Anjum Gupta)
• Section 5 Notes, February 6, 2004, by Kristin Branson 1 FOL Terminology Review
• Discussion 6 notes. February 13th (Anjum Gupta)
• Discussion 8 Notes Prepared by: Anjum Gupta
• CSE134A Section -Notes on Debugging October 11, 2002
• In Social Science Research, Technical Systems and Cooperative Work: Beyond the Great Divide, edited by Geo rey Bowker, Les Gasser, Leigh Star and William Turner, Erlbaum, 1997, pages 27{56.
• KDD'99 Competition: Knowledge Discovery Contest Jim Georges, Ph.D.
• Maximum Likelihood, Logistic Regression, and Stochastic Gradient Training
• The Expectation-Maximization Algorithm Charles Elkan
• Predictive analytics and data mining Charles Elkan
• Transductive Inference for Text Classification using Support Vector Machines
• Enhanced Hypertext Categorization using Hyperlinks
• Nucleic Acids Research, 2008, 15 doi:10.1093/nar/gkn862
• Modeling Word Burstiness Using the Dirichlet Distribution Rasmus E. Madsen rem@imm.dtu.dk
• Using the Triangle Inequality to Accelerate Charles Elkan ELKAN@CS.UCSD.EDU
• Perceptron Classifiers Charles Elkan
• Nearest Neighbor Classification Charles Elkan
• Log-linear models and conditional random fields
• Mixture Models Charles Elkan
• Obtaining Calibrated Probability Estimates from Support Vector Machines
• Effect of Boosting in BWI David Kauchak
• AdaBoost for Query-by-Example in Text Jonathan Ultis Computer Science & Engr. Dept. (0014)
• Reducing multiclass to binary by coupling probability estimates
• Nearest Neighbor Classification Charles Elkan
• Department of Computer Science and Engineering CSE 151 University of California, San Diego Fall 2008
• Reinforcement Learning Charles Elkan
• Adversarial Classification Nilesh Dalvi, Pedro Domingos, Mausam, Sumit Sanghai, Deepak
• Recommender Systems New Approaches with Netflix Dataset
• PageRank for Product Image Search Yushi Jing1 Shumeet Baluja2
• The Lane's Gifts v. Google By Alexander Tuzhilin
• DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING UNIVERSITY OF CALIFORNIA, SAN DIEGO
• Section 7 Notes, February 20, 2004, by Kristin Branson 1 Situation Calculus Terminology Review
• Image Analogies Aaron Hertzman Charles E. Jacobs Nuria Oliver
• Department of Computer Science and Engineering CSE 150 University of California, San Diego Winter 2004
• Outline Motivation Dimensionality reduction Experimental setup Results Discussion References Dimensionality reduction for supervised learning
• Segmentation using eigenvectors: a unifying view
• Text mining and topic models Charles Elkan
• CSE 151, Fall 2008 Assignment 5
• Log-linear models and conditional random fields
• Matlab Tutorial (9/29/08) by Y. Albert Park 1. Basic commands
• CSE291: Statistical Learning Lecture #7 University of California, San Diego Tuesday, 25 January 2005
• Home | Register | Home Delivery | Site Map | Archives | Print Edition | Advertise | Feedback jobs * cars * homes * rentals
• CSE291: Statistical Learning Lecture #12 University of California, San Diego Thursday, 10 February 2005
• ' ( ) 0 1 2 3 4 ( 5 ( 6 7 ( 8 3 4 9 3 ' 2 @ 8 2 @ 4 4 3 ( 5 1 5 A 5 1 3 B @ C D E F G
• Information extraction with HMMs and shrinkage
• Message table msg id parent id subject
• DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING UNIVERSITY OF CALIFORNIA, SAN DIEGO
• Topic Models Charles Elkan
• Notes on Machine Learning Projects Charles Elkan
• Published: From Animals to Animats, Fourth International Conference
• From Promoter Sequence to Expression: A Probabilistic
• Nearest Neighbor Classification Charles Elkan
• Discovering Markov Blankets: Finding Independencies Among Variables
• Text mining and topic models Charles Elkan
• Logistic Regression and Stochastic Gradient Training
• A brief MySQL tutorial CSE 134A: Web Service Design and
• CSE291: Statistical Learning Lecture #1 University of California, San Diego
• Section 4 Notes, January 30, 2004, by Kristin Branson 1 Problem Statement and Terminology
• Maximum Entropy Markov Models for Information Extraction and Segmentation
• Using Maximum Entropy forUsing Maximum Entropy for T e xt C l assific ationT e xt C l assific ation
• Automatic Identification of User Goals in Web Search [WWW'05]
• Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
• CSE 151 Fall 2007 Assignment 5
• Functional programming From Wikipedia, the free encyclopedia.
• Boosted Wrapper InductionBoosted Wrapper Induction Dayne Freitag and Nicholas Kushmerick
• Document Clustering Using Word Clusters via the Information Bottleneck Method
• Connecting to MySQL from PHP $link = mysql_connect($hostname, $username, $password)
• Automatic Music Annotation A Research Exam by
• Google News Personalization: Scalable Online Collaborative Filtering
• Probabilistic Latent Semantic Indexing
• Aldebaro Klautau Reducing Multiclass to Binary: A
• A random walks perspective on maximizing satisfaction and profit
• Sequential Cost-Sensitive Decision-Making with
• Using News Articles to Predict Stock Price Movements
• Strategy Acquisition for the Game Othello Based on Reinforcement Learning
• CSE291: Statistical Learning Lecture #5 University of California, San Diego Thursday, 27 January 2005
• Complex decisions Chapter 17, Sections 13
• Nearest Neighbor Classification Charles Elkan
• "# $%& '# (!0)1 02 3#0(4 657 82289@'A
• Log-Linear Models and Conditional Random Fields
• DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING UNIVERSITY OF CALIFORNIA, SAN DIEGO
• Assignment 1 Web Board Summary Revised January 21, 2004, by Kristin Branson
• Web-scale information retrieval and data mining List of papers
• Mining Concurrent Text and Time Series Victor Lavrenko, Matt Schmill, Dawn Lawrie, Paul Ogilvie,
• A Natural Law of Succession Paper by Eric Sven Ristad,
• Will Reasoning Improve By Nicolaas J. Vriend
• Maximum Likelihood Methods Charles Elkan
• Perceptron Classifiers Charles Elkan
• Chisquared categorical
• Department of Computer Science and Engineering CSE 151 University of California, San Diego Fall 2008
• A Blueprint for Building Web Sites Using the Microsoft Windows Platform
• CSE291: Statistical Learning Lecture #2 University of California, San Diego
• CSE291: Statistical Learning Lecture #8 University of California, San Diego Thursday, 27 January 2005
• Week 2 Section (10/6/08) by Y. Albert Park Perceptron algorithm
• Logistic Regression and Stochastic Gradient Training
• Kernel Methods Charles Elkan
• CSE 150 Discussion 3 Jan 23rd Anjum Gupta
• Online and Batch Learning of Pseudo-Metrics Shai Shalev-Shwartz1 Yoram Singer1 Andrew Ng2
• Efficient Exact k-NN and Non-parametric Classification
• Predictive analytics and data mining Charles Elkan