MLJ 2002

54 papers

A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles Shlomo Dubnov, Ran El-Yaniv, Yoram Gdalyahu, Elad Schneidman, Naftali Tishby, Golan Yona

A Probabilistic Framework for SVM Regression and Error Bar Estimation Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin Brown

A Simple Decomposition Method for Support Vector Machines Chih-Wei Hsu, Chih-Jen Lin

A Simple Method for Generating Additive Clustering Models with Limited Complexity Michael D. Lee

A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

An Analytic Center Machine Theodore B. Trafalis, Alexander M. Malyscheff

Bayesian Clustering by Dynamics Marco Ramoni, Paola Sebastiani, Paul R. Cohen

Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities Peter Sollich

Bayesian Treed Models Hugh A. Chipman, Edward I. George, Robert E. McCulloch

Boosting Methods for Regression Nigel Duffy, David P. Helmbold

Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts Amy McGovern, J. Eliot B. Moss, Andrew G. Barto

Choosing Multiple Parameters for Support Vector Machines Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee

Continuous-Action Q-Learning José del R. Millán, Daniele Posenato, Eric Dedieu

Convergence of a Generalized SMO Algorithm for SVM Classifier Design S. Sathiya Keerthi, Elmer G. Gilbert

Decision Region Connectivity Analysis: A Method for Analyzing High-Dimensional Classifiers Ofer Melnik

Efficient SVM Regression Training with SMO Gary William Flake, Steve Lawrence

Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates Tom Bylander

Feasible Direction Decomposition Algorithms for Training Support Vector Machines Pavel Laskov

Feature Generation Using General Constructor Functions Shaul Markovitch, Dan Rosenstein

Finite-Time Analysis of the Multiarmed Bandit Problem Peter Auer, Nicolò Cesa-Bianchi, Paul Fischer

Gene Selection for Cancer Classification Using Support Vector Machines Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik

Hierarchical Learning in Polynomial Support Vector Machines Sebastian Risau-Gusman, Mirta B. Gordon

Kernel Matching Pursuit Pascal Vincent, Yoshua Bengio

Kernel-Based Reinforcement Learning Dirk Ormoneit, Saunak Sen

Large Scale Kernel Regression via Linear Programming Olvi L. Mangasarian, David R. Musicant

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction José M. Peña, José Antonio Lozano, Pedro Larrañaga

Linear Programming Boosting via Column Generation Ayhan Demiriz, Kristin P. Bennett, John Shawe-Taylor

Logistic Regression, AdaBoost and Bregman Distances Michael Collins, Robert E. Schapire, Yoram Singer

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachlan, Christine E. McLaren

Metric-Based Methods for Adaptive Model Selection and Regularization Dale Schuurmans, Finnegan Southey

Model Selection and Error Estimation Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi

Model Selection for Small Sample Regression Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio

Near-Optimal Reinforcement Learning in Polynomial Time Michael J. Kearns, Satinder Singh

On a Connection Between Kernel PCA and Metric Multidimensional Scaling Christopher K. I. Williams

On Average Versus Discounted Reward Temporal-Difference Learning John N. Tsitsiklis, Benjamin Van Roy

On the Dual Formulation of Regularized Linear Systems with Convex Risks Tong Zhang

On the Existence of Linear Weak Learners and Applications to Boosting Shie Mannor, Ron Meir

On the Learnability and Design of Output Codes for Multiclass Problems Koby Crammer, Yoram Singer

PAC Analogues of Perceptron and Winnow via Boosting the Margin Rocco A. Servedio

Reinforcement Learning for Call Admission Control and Routing Under Quality of Service Constraints in Multimedia Networks Hui Tong, Timothy X. Brown

Risk-Sensitive Reinforcement Learning Oliver Mihatsch, Ralph Neuneier

Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces Gunnar Rätsch, Ayhan Demiriz, Kristin P. Bennett

Statistical Properties and Adaptive Tuning of Support Vector Machines Yi Lin, Grace Wahba, Hao Zhang, Yoonkyung Lee

Structural Modelling with Sparse Kernels Steve R. Gunn, Jaz S. Kandola

Structure in the Space of Value Functions David J. Foster, Peter Dayan

Support Vector Machines for Classification in Nonstandard Situations Yi Lin, Yoonkyung Lee, Grace Wahba

Technical Update: Least-Squares Temporal Difference Learning Justin A. Boyan

Text Categorization with Support Vector Machines. How to Represent Texts in Input Space? Edda Leopold, Jörg Kindermann

The Lagging Anchor Algorithm: Reinforcement Learning in Two-Player Zero-Sum Games with Imperfect Information Fredrik A. Dahl

The Relaxed Online Maximum Margin Algorithm Yi Li, Philip M. Long

Theoretical and Experimental Evaluation of the Subspace Information Criterion Masashi Sugiyama, Hidemitsu Ogawa

Theory Revision with Queries: DNF Formulas Judy Goldsmith, Robert H. Sloan, György Turán

Training Invariant Support Vector Machines Dennis DeCoste, Bernhard Schölkopf

Variable Resolution Discretization in Optimal Control Rémi Munos, Andrew W. Moore