Tom Fawcett, Nina Mishra
Proceedings of the 20th International Conference on Machine Learning
ICML-2003, 2003.

@proceedings{ICML-2003,
	address       = "Washington, District of Columbia, USA",
	editor        = "Tom Fawcett and Nina Mishra",
	isbn          = "1-57735-189-4",
	publisher     = "{AAAI Press}",
	title         = "{Proceedings of the 20th International Conference on Machine Learning}",
	year          = 2003,
}

Contents (117 items)

43 ×#learning
13 ×#classification
10 ×#kernel
10 ×#using
8 ×#algorithm
8 ×#multi
7 ×#analysis
7 ×#modelling
7 ×#performance
6 ×#approximate

ICML-2003-AltunTH #markov

Hidden Markov Support Vector Machines (YA, IT, TH), pp. 3–10.

ICML-2003-Bar-HillelHSW #distance #equivalence #learning #using

Learning Distance Functions using Equivalence Relations (ABH, TH, NS, DW), pp. 11–18.

ICML-2003-BaramEL #algorithm #learning #online

Online Choice of Active Learning Algorithms (YB, REY, KL), pp. 19–26.

ICML-2003-BerardiCEM #analysis #layout #learning #logic programming #source code

Learning Logic Programs for Layout Analysis Correction (MB, MC, FE, DM), pp. 27–34.

ICML-2003-Bi #multi #programming

Multi-Objective Programming in SVMs (JB), pp. 35–42.

ICML-2003-BiB #fault

Regression Error Characteristic Curves (JB, KPB), pp. 43–50.

ICML-2003-Bouckaert #algorithm #learning #testing

Choosing Between Two Learning Algorithms Based on Calibrated Tests (RRB), pp. 51–58.

ICML-2003-Brinker #learning

Incorporating Diversity in Active Learning with Support Vector Machines (KB), pp. 59–66.

ICML-2003-BrownW #ambiguity #composition #learning #network #using

The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods (GB, JLW), pp. 67–74.

ICML-2003-CerquidesM #learning #modelling #naive bayes

Tractable Bayesian Learning of Tree Augmented Naive Bayes Models (JC, RLdM), pp. 75–82.

ICML-2003-ConitzerS #algorithm #learning #multi #named #self

AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents (VC, TS), pp. 83–90.

ICML-2003-ConitzerS03a #bound #framework #game studies #named

BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games (VC, TS), pp. 91–98.

ICML-2003-CozmanCC #learning #modelling

Semi-Supervised Learning of Mixture Models (FGC, IC, MCC), pp. 99–106.

ICML-2003-CumbyR #kernel #learning #on the #relational

On Kernel Methods for Relational Learning (CMC, DR), pp. 107–114.

ICML-2003-DeCosteM #approximate #classification #incremental #kernel #performance

Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors (DD, DM), pp. 115–122.

ICML-2003-DriessensR #learning #relational

Relational Instance Based Regression for Relational Reinforcement Learning (KD, JR), pp. 123–130.

ICML-2003-Duff #design

Design for an Optimal Probe (MOD), pp. 131–138.

ICML-2003-Duff03a #approximate #markov

Diffusion Approximation for Bayesian Markov Chains (MOD), pp. 139–146.

ICML-2003-Elkan #difference #using

Using the Triangle Inequality to Accelerate k-Means (CE), pp. 147–153.

ICML-2003-EngelMM #approach #difference #learning #process

Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (YE, SM, RM), pp. 154–161.

ICML-2003-Even-DarMM #learning

Action Elimination and Stopping Conditions for Reinforcement Learning (EED, SM, YM), pp. 162–169.

ICML-2003-FanLM

Utilizing Domain Knowledge in Neuroevolution (JF, RL, RM), pp. 170–177.

ICML-2003-FernB #lazy evaluation

Boosting Lazy Decision Trees (XZF, CEB), pp. 178–185.

ICML-2003-FernB03a #approach #clustering #random

Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach (XZF, CEB), pp. 186–193.

ICML-2003-Flach #comprehension #geometry #machine learning #metric

The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics (PAF), pp. 194–201.

ICML-2003-FurnkranzF #analysis #evaluation #metric

An Analysis of Rule Evaluation Metrics (JF, PAF), pp. 202–209.

ICML-2003-GargR #learning

Margin Distribution and Learning (AG, DR), pp. 210–217.

ICML-2003-GeibelW #learning

Perceptron Based Learning with Example Dependent and Noisy Costs (PG, FW), pp. 218–225.

ICML-2003-GhavamzadehM #algorithm #policy

Hierarchical Policy Gradient Algorithms (MG, SM), pp. 226–233.

ICML-2003-Graepel #difference #equation #linear #process

Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations (TG), pp. 234–241.

ICML-2003-GreenwaldH #correlation

Correlated Q-Learning (AG, KH), pp. 242–249.

ICML-2003-Harrington #algorithm #collaboration #online #ranking #using

Online Ranking/Collaborative Filtering Using the Perceptron Algorithm (EFH), pp. 250–257.

ICML-2003-IsaacS #learning

Goal-directed Learning to Fly (AI, CS), pp. 258–265.

ICML-2003-Jaeger #classification #concept #probability

Probabilistic Classifiers and the Concepts They Recognize (MJ), pp. 266–273.

ICML-2003-JensenNH #bias #relational

Avoiding Bias when Aggregating Relational Data with Degree Disparity (DJ, JN, MH), pp. 274–281.

ICML-2003-JinYZH #algorithm #exponential #performance #scalability

A Faster Iterative Scaling Algorithm for Conditional Exponential Model (RJ, RY, JZ, AGH), pp. 282–289.

ICML-2003-Joachims #clustering #graph #learning

Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.

ICML-2003-JohnsonTG #crawling #evolution #web

Evolving Strategies for Focused Web Crawling (JJ, KT, CLG), pp. 298–305.

ICML-2003-KakadeKL #metric

Exploration in Metric State Spaces (SK, MJK, JL), pp. 306–312.

ICML-2003-KalousisH

Representational Issues in Meta-Learning (AK, MH), pp. 313–320.

ICML-2003-KashimaTI #graph #kernel

Marginalized Kernels Between Labeled Graphs (HK, KT, AI), pp. 321–328.

ICML-2003-KaskiP #analysis

Informative Discriminant Analysis (SK, JP), pp. 329–336.

ICML-2003-KennedyJ #learning #problem

Characteristics of Long-term Learning in Soar and its Application to the Utility Problem (WGK, KADJ), pp. 337–344.

ICML-2003-KirshnerPS #learning #permutation

Unsupervised Learning with Permuted Data (SK, SP, PS), pp. 345–352.

ICML-2003-KlautauJO #classification #comparison #kernel #modelling

Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers (AK, NJ, AO), pp. 353–360.

ICML-2003-KondorJ #kernel #set

A Kernel Between Sets of Vectors (RK, TJ), pp. 361–368.

ICML-2003-KotnikK #learning #self

The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy (CK, JKK), pp. 369–375.

ICML-2003-KrawiecB #learning #synthesis #visual notation

Visual Learning by Evolutionary Feature Synthesis (KK, BB), pp. 376–383.

ICML-2003-KrishnapuramCJ #classification #documentation

Classification of Text Documents Based on Minimum System Entropy (RK, KPC, SJ), pp. 384–391.

ICML-2003-KubicaMCS #analysis #collaboration #graph #performance #query

Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries (JK, AWM, DC, JGS), pp. 392–399.

ICML-2003-KwokT #kernel #learning

Learning with Idealized Kernels (JTK, IWT), pp. 400–407.

ICML-2003-KwokT03a #kernel #problem

The Pre-Image Problem in Kernel Methods (JTK, IWT), pp. 408–415.

ICML-2003-LachicheF #classification #multi #probability #using

Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves (NL, PAF), pp. 416–423.

ICML-2003-LagoudakisP #classification #learning

Reinforcement Learning as Classification: Leveraging Modern Classifiers (MGL, RP), pp. 424–431.

ICML-2003-LangleyGBS #induction #modelling #process #robust

Robust Induction of Process Models from Time-Series Data (PL, DG, SDB, KS), pp. 432–439.

ICML-2003-LaudD #analysis #learning

The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping (AL, GD), pp. 440–447.

ICML-2003-LeeL #learning #using

Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (WSL, BL), pp. 448–455.

ICML-2003-LeskovecS #dataset #linear #programming

Linear Programming Boosting for Uneven Datasets (JL, JST), pp. 456–463.

ICML-2003-LiWL #classification #generative #keyword #probability #using

Text Classification Using Stochastic Keyword Generation (CL, JRW, HL), pp. 464–471.

ICML-2003-LiY #analysis #categorisation #classification

A Loss Function Analysis for Classification Methods in Text Categorization (FL, YY), pp. 472–479.

ICML-2003-LingY #ranking

Decision Tree with Better Ranking (CXL, RJY), pp. 480–487.

ICML-2003-LiuLCM #clustering #evaluation #feature model

An Evaluation on Feature Selection for Text Clustering (TL, SL, ZC, WYM), pp. 488–495.

ICML-2003-LuG #classification

Link-based Classification (QL, LG), pp. 496–503.

ICML-2003-Mamitsuka #analysis

Hierarchical Latent Knowledge Analysis for Co-occurrence Data (HM), pp. 504–511.

ICML-2003-MannorRG #performance #policy

The Cross Entropy Method for Fast Policy Search (SM, RYR, YG), pp. 512–519.

ICML-2003-MarchandSSS #set

The Set Covering Machine with Data-Dependent Half-Spaces (MM, MS, JST, MS), pp. 520–527.

ICML-2003-McGovernJ #identification #learning #multi #predict #relational #using

Identifying Predictive Structures in Relational Data Using Multiple Instance Learning (AM, DJ), pp. 528–535.

ICML-2003-McMahanGB #cost analysis

Planning in the Presence of Cost Functions Controlled by an Adversary (HBM, GJG, AB), pp. 536–543.

ICML-2003-Mesterharm #algorithm #multi #using

Using Linear-threshold Algorithms to Combine Multi-class Sub-experts (CM), pp. 544–551.

ICML-2003-MooreW #learning #network

Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning (AWM, WKW), pp. 552–559.

ICML-2003-Munos #approximate #bound #fault #policy

Error Bounds for Approximate Policy Iteration (RM), pp. 560–567.

ICML-2003-OngS #kernel #machine learning

Machine Learning with Hyperkernels (CSO, AJS), pp. 568–575.

ICML-2003-OntanonP #learning #multi

Justification-based Multiagent Learning (SO, EP), pp. 576–583.

ICML-2003-PavlovPPU #modelling

Mixtures of Conditional Maximum Entropy Models (DP, AP, DMP, LHU), pp. 584–591.

ICML-2003-PerkinsT #feature model #online #using

Online Feature Selection using Grafting (SP, JT), pp. 592–599.

ICML-2003-PorterEHT #classification #order #scalability #statistics

Weighted Order Statistic Classifiers with Large Rank-Order Margin (RBP, DE, DRH, JT), pp. 600–607.

ICML-2003-RavindranB

Relativized Options: Choosing the Right Transformation (BR, AGB), pp. 608–615.

ICML-2003-RennieSTK #classification #naive bayes

Tackling the Poor Assumptions of Naive Bayes Text Classifiers (JDR, LS, JT, DRK), pp. 616–623.

ICML-2003-RichardsonD #learning #multi

Learning with Knowledge from Multiple Experts (MR, PMD), pp. 624–631.

ICML-2003-RivestP #network

Combining TD-learning with Cascade-correlation Networks (FR, DP), pp. 632–639.

ICML-2003-RosipalTM #classification #kernel #linear

Kernel PLS-SVC for Linear and Nonlinear Classification (RR, LJT, BM), pp. 640–647.

ICML-2003-RuckertK #learning #probability

Stochastic Local Search in k-Term DNF Learning (UR, SK), pp. 648–655.

ICML-2003-RussellZ #learning

Q-Decomposition for Reinforcement Learning Agents (SJR, AZ), pp. 656–663.

ICML-2003-SalakhutdinovR #adaptation #bound #optimisation

Adaptive Overrelaxed Bound Optimization Methods (RS, STR), pp. 664–671.

ICML-2003-SalakhutdinovRG #optimisation

Optimization with EM and Expectation-Conjugate-Gradient (RS, STR, ZG), pp. 672–679.

ICML-2003-SchoknechtM #algorithm #performance

TD(0) Converges Provably Faster than the Residual Gradient Algorithm (RS, AM), pp. 680–687.

ICML-2003-SebbanJ #approach #grammar inference #on the #semistructured data #statistics

On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data (MS, JCJ), pp. 688–695.

ICML-2003-ShihRCK #statistics

Text Bundling: Statistics Based Data-Reduction (LS, JDR, YHC, DRK), pp. 696–703.

ICML-2003-SiJ #collaboration #flexibility

Flexible Mixture Model for Collaborative Filtering (LS, RJ), pp. 704–711.

ICML-2003-SinghLJPS #learning #predict

Learning Predictive State Representations (SPS, MLL, NKJ, DP, PS), pp. 712–719.

ICML-2003-SrebroJ #approximate #rank

Weighted Low-Rank Approximations (NS, TSJ), pp. 720–727.

ICML-2003-StimpsonG #approach #learning #social

Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining (JLS, MAG), pp. 728–735.

ICML-2003-Strens #optimisation

Evolutionary MCMC Sampling and Optimization in Discrete Spaces (MJAS), pp. 736–743.

ICML-2003-TaskarWK #learning #testing

Learning on the Test Data: Leveraging Unseen Features (BT, MFW, DK), pp. 744–751.

ICML-2003-ValentiniD #bias

Low Bias Bagged Support Vector Machines (GV, TGD), pp. 752–759.

ICML-2003-VishwanathanSM

SimpleSVM (SVNV, AJS, MNM), pp. 760–767.

ICML-2003-VovkNG #online #testing

Testing Exchangeability On-Line (VV, IN, AG), pp. 768–775.

ICML-2003-WangD #learning #modelling #policy

Model-based Policy Gradient Reinforcement Learning (XW, TGD), pp. 776–783.

ICML-2003-WangSPZ #learning #modelling #principle

Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.

ICML-2003-WiewioraCE #learning

Principled Methods for Advising Reinforcement Learning Agents (EW, GWC, CE), pp. 792–799.

ICML-2003-WinnerV #learning #named

DISTILL: Learning Domain-Specific Planners by Example (EW, MMV), pp. 800–807.

ICML-2003-WongMCW #detection #network

Bayesian Network Anomaly Pattern Detection for Disease Outbreaks (WKW, AWM, GFC, MMW), pp. 808–815.

ICML-2003-WuC #adaptation #learning

Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning (GW, EYC), pp. 816–823.

ICML-2003-WuS #optimisation

New í-Support Vector Machines and their Sequential Minimal Optimization (XW, RKS), pp. 824–831.

ICML-2003-YamadaSU #network

Cross-Entropy Directed Embedding of Network Data (TY, KS, NU), pp. 832–839.

ICML-2003-YamadaSYT #data-driven #database #induction #standard

Decision-tree Induction from Time-series Data Based on a Standard-example Split Test (YY, ES, HY, KT), pp. 840–847.

ICML-2003-YanDMW #approximate #classification #optimisation #performance #statistics

Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic (LY, RHD, MM, RHW), pp. 848–855.

ICML-2003-YuL #feature model #performance

Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution (LY, HL), pp. 856–863.

ICML-2003-ZhaZ

Isometric Embedding and Continuum ISOMAP (HZ, ZZ), pp. 864–871.

ICML-2003-Zhang #kernel #learning #metric #multi #representation #scalability #towards

Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation (ZZ), pp. 872–879.

ICML-2003-ZhangH #learning #taxonomy

Learning from Attribute Value Taxonomies and Partially Specified Instances (JZ, VH), pp. 880–887.

ICML-2003-ZhangJYH #approximate #categorisation #scalability

Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization (JZ, RJ, YY, AGH), pp. 888–895.

ICML-2003-ZhangXC #adaptation #learning

Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning (YZ, WX, JPC), pp. 896–903.

ICML-2003-ZhangY #convergence #on the

On the Convergence of Boosting Procedures (TZ, BY), pp. 904–911.

ICML-2003-ZhuGL #learning #using

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.

ICML-2003-ZhuWC #dataset #scalability

Eliminating Class Noise in Large Datasets (XZ, XW, QC), pp. 920–927.

ICML-2003-Zinkevich #online #programming

Online Convex Programming and Generalized Infinitesimal Gradient Ascent (MZ), pp. 928–936.