Avanti Eating Club

Ensemble Methods

• Bayesian Averaging of Classifiers and the Overfitting Problem/ Pedro Domingos
• Bounds on the Generalization Performance of Kernel Machine Ensembles/ Theodoros Evgeniou, Luis Perez-Breva, Massimiliano Pontil, and Tomaso Poggio
• A Normative Examination of Ensemble Learning Algorithms/ David M. Pennock, Pedrito Maynard-Reid II, C. Lee Giles, and Eric Horvitz
• Selective Voting for Perceptron-like Online Learning/ Yi Li
• Online Ensemble Learning: An Empirical Study/ Alan Fern and Robert Givan
• Voting Nearest-Neighbor Subclassifiers/ Miroslav Kubat and Martin Cooperson, Jr.
• Lightweight Rule Induction/ Sholom M. Weiss and Nitin Indurkhya
• MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data/ Cenk Kaynak and Ethem Alpaydin
• ``Boosting'' a Positive-Data-Only Learner/ Andrew Mitchell

Knowledge Transfer in Learning

• Empirical Bayes for Learning to Learn/ Tom Heskes
• Disciple-COA: From Agent Programming to Agent Teaching/ M. Boicu, G. Tecuci, D. Marcu, M. Bowman, P. Shyr, F. Ciucu, and C. Levcovici

Unsupervised Learning

• Finding Variational Structure in Data by Cross-Entropy Optimization/ Matthew Brand
• An Approach to Data Reduction and Clustering with Theoretical Guarantees/ Partha Niyogi and Narendra Karmarkar
• X-means: Extending K-means with Efficient Estimation of the Number of Clusters/ Dan Pelleg and Andrew Moore
• Model Selection Criteria for Learning Belief Nets: An Empirical Comparison/ Tim Van Allen and Russ Greiner Anomaly Detection
• Local Expert Autoassociators for Anomaly Detection/ Geoffrey G. Towell
• Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data/ Terran Lane and Carla E. Brodley
• Anomaly Detection over Noisy Data using Learned Probability Distributions/ Eleazar Eskin

Beefeaters Eating Club

Learning from Text

• Approximate Dimension Equalization in Vector-based Information Retrieval/ Fan Jiang and Michael L. Littman
• Using Error-Correcting Codes for Text Classification/ Rayid Ghani
• Support Vector Machine Active Learning with Applications to Text Classification/ Simon Tong and Daphne Koller
• Learning to Select Text Databases with Neural Nets/ Yong S. Choi and Suk I. Yoo
• Combining Multiple Learning Strategies for Effective Cross Validation/ Yiming Yang, Thomas Ault, and Thomas Pierce
• Detecting Concept Drift with Support Vector Machines/ Ralf Klinkenberg and Thorsten Joachims
• Learning to Probabilistically Identify Authoritative Documents/ David Cohn and Huan Chang
• Improving Short-Text Classification using Unlabeled Background Knowledge to Assess Document Similarity/ Sarah Zelikovitz and Haym Hirsh
• Automatically Extracting Features for Concept Learning from the Web/ William W. Cohen

Learning Structures and Relations

• Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space/ Alberto Paccanaro and Geoffrey E. Hinton
• Classification of Individuals with Complex Structure/ A. F. Bowers, C. Giraud-Carrier, and J. W. Lloyd
• Data as Ensembles of Records: Representation and Comparison/ Nicholas R. Howe
• Learning Horn Expressions with LogAn-H/ Roni Khardon
• Ideal Theory Refinement under Object Identity/ Floriana Esposito, Nicola Fanizzi, Stefano Ferilli, and Giovanni Semeraro

Computational Scientific Discovery

• Enhancing the Plausibility of Law Equation Discovery/ Takashi Washio, Hiroshi Motoda, and Yuji Niwa
• Discovering the Structure of Partial Differential Equations from Example Behaviour/ Ljupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan Whiteley, and David Gavaghan
• Automatic Identification of Mathematical Concepts/ Simon Colton, Alan Bundy, and Toby Walsh
• A Divide and Conquer Approach to Learning from Prior Knowledge/ Eric Chown and Thomas G. Dietterich

Middle Earth Eating Club

Continuous-State Reinforcement Learning

• Practical Reinforcement Learning in Continuous Spaces/ William D. Smart and Leslie Pack Kaelbling
• Combining Reinforcement Learning with a Local Control Algorithm/ Jette Randlov, Andrew G. Barto, and Michael T. Rosenstein
• Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning/ Gerald DeJong
• Rates of Convergence for Variable Resolution Schemes in Optimal Control/ Remi Munos and Andrew W. Moore
• Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning/ Stuart I. Reynolds

Supervised Learning

• The Effect of the Input Density Distribution on Kernel-based Classifiers/ Christopher K. I. Williams and Matthias Seeger
• Classification with Multiple Latent Variable Models using Maximum Entropy Discrimination/ Machiel Westerdijk and Wim Wiegerinck
• Duality and Geometry in SVM Classifiers/ Kristin P. Bennett and Erin J. Bredensteiner

Learning Motor Skills

• Behavioral Cloning of Student Pilots with Modular Neural Networks/ Charles W. Anderson, Bruce A. Draper, and David A. Peterson
• Learning to Fly: An Application of Hierarchical Reinforcement Learning/ Malcolm Ryan and Mark Reid
• On-line Learning for Humanoid Robot Systems/ Jorg Conradt, Gaurav Tevatia, Sethu Vijaya- kumar, and Stefan Schaal
• Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning/ Jun Morimoto and Kenji Doya
• An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control/ Dean F. Hougen, Maria Gini, and James Slagle
• Learning Multiple Models for Reward Maximization/ Dani Goldberg and Maja J. Mataric
• Learning in Non-stationary Conditions: A Control Theoretic Approach/ Jefferson Coelho and Rod Grupen
• Locally Weighted Projection Regression: An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space/ Sethu Vijayakumar and Stefan Schaal

Regression Learning

• Learning to Predict Performance from Formula Modeling and Training Data/ Bryan Singer and Manuela Veloso
• Partial Linear Trees/ Luis Torgo

Avanti Eating Club

Empirical Studies of Induction

• A Quantification of Distance Bias Between Evaluation Metrics In Classification/ Ricardo Vilalta and Daniel Oblinger
• Analyzing Relational Learning in the Phase Transition Framework/ Attilio Giordana, Lorenza Saitta, Michele Sebag, and Marco Botta
• Meta-Learning by Landmarking Various Learning Algorithms/ Bernhard Pfahringer, Hilan Bensusan, and Christophe Giraud-Carrier
• Characterizing Model Errors and Differences/ Stephen D. Bay and Michael J. Pazzani

Theoretical Analyses of Induction

• A Unified Bias-Variance Decomposition and its Applications/ Pedro Domingos
• Generalized Average-Case Analyses of the Nearest Neighbor Algorithm/ Seishi Okamoto and Nobuhiro Yugami
• Why Discretization Works for Naive Bayesian Classifiers/ Chun-Nan Hsu, Hung-Ju Huang, and Tzu-Tsung Wong
• Predicting the Generalization Performance of Cross Validatory Model Selection Criteria/ Tobias Scheffer

COLT Posters

• On the Convergence Rate of Good-Turing Estimators/ David McAllester and Robert E. Schapire
• Decision Tree Approximations of Boolean Functions/ Dinesh Mehta and Vijay Raghavan
• Computable Shell Decomposition Bounds/ John Langford and David McAllester
• Continuous Drifting Games/ Yoav Freund and Manfred Opper
• Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations/ Sanjay Jain, Efim Kinber and Rolf Wiehagen
• Average-Case Complexity of Learning Polynomials/ Frank Stephan and Thomas Zeugmann
• Generalization Bounds for Decision Trees/ Yishay Mansour and David McAllester
• The Role of Critical Sets in Vapnik-Chervonenkis Theory/ Nicolas Vayatis
• Statistical Sufficiency for Classes in Empirical $L_2$ Spaces/ Shahar Mendelson and Naftali Tishby
• Relative Expected Instantaneous Loss Bounds/ Juergen Forster and Manfred Warmuth
• Adaptive and Self-Confident On-Line Learning Algorithms/ Peter Auer and Claudio Gentile
• An Improved On-line Algorithm for Learning Linear Evaluation Functions/ Peter Auer
• On the Learnability and Design of Output Codes for Multiclass Problems/ Koby Crammer and Yoram Singer
• PAC Analogues of Perceptron and Winnow via Boosting the Margin/ Rocco A. Servedio

Beefeaters Eating Club

Efficiency Issues in Induction

• A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets/ Paul Komarek and Andrew Moore
• Dimension Reduction Techniques for Training Polynomial Networks/ William M. Campbell, Kari Torkkola, and Sreeram V. Balakrishnan
• Complete Cross-Validation for Nearest Neighbor Classifiers/ Matthew Mullin and Rahul Sukthankar
• Estimating the Generalization Performance of an SVM Efficiently/ Thorsten Joachims
• Sparse Greedy Matrix Approximation for Machine Learning/ Alex J. Smola and Bernhard Scholkopf

COLT Posters

• Logistic Regression, AdaBoost and Bregman Distances/ Michael Collins, Robert E. Schapire and Yoram Singer
• Barrier Boosting/ G. Raetsch, M. Warmuth, S. Mika, T. Onoda, S. Lemm, and K.-R. Mueller
• MadaBoost: A Modification of AdaBoost/ Carlos Domingo and Osamu Watanabe
• Localized Boosting/ Ron Meir and Ran El-Yaniv and Shai Ben-David
• Improving Algorithms for Boosting/ Javed A. Aslam
• Leveraging for Regression/ Nigel Duffy and David Helmbold
• Boosting Using Branching Programs/ Yishay Mansour and David McAllester
• The Precision of Query Points as a Resource for Learning Convex Polytopes with Membership Queries/ Paul Goldberg and Stephen Kwek
• Improved Algorithms for Theory Revision with Queries/ Judy Goldsmith, Robert H. Sloan, Balazs Szorenyi, and Gyorgy Turan
• Abstract Combinatorial Characterizations of Exact Learning via Queries/ Jose Luis Balcazar, Jorge Castro, and David Guijarro
• The Complexity of Densest Region Detection/ Shai Ben-David, Nadav Eiron, and Hans Simon
• On the Difficulty of Approximately Maximizing Agreements/ Shai Ben-David, Nadav Eiron, and Philip M. Long
• Hardness Results for General Two-Layer Neural Networks/ Christian Kuhlmann
• Model Selection and Error Estimation/ Peter L. Bartlett, Stephane Boucheron, and Gabor Lugosi
• Generalisation Error Bounds for Sparse Linear Classifiers/ Thore Graepel, Ralf Herbrich, and John Shawe-Taylor
• Sparsity vs. Large Margins for Linear Classifiers/ Ralf Herbrich, Thore Graepel, and John Shawe-Taylor
• Entropy Numbers of Linear Function Classes/ Robert C. Williamson, Alex J. Smola, and Bernhard Scholkopf

Bollard Eating Club

COLT Posters

• On the Efficiency of Noise-Tolerant PAC Algorithms Derived from Statistical Queries/ Jeffrey Jackson
• The Minimax Strategy for Gaussian Density Estimation/ Eiji Takimoto and Manfred Warmuth
• Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning/ Peter L. Bartlett and Jonathan Baxter
• Bias-Variance Error Bounds for Temporal Difference Updates/ Michael Kearns and Satinder Singh

UAI Posters

• The Complexity of Decentralized Control of Markov Decision Processes/ Daniel Bernstein, Shlomo Zilberstein, and Neil Immerman
• Approximately Optimal Monitoring of Plan Preconditions/ Craig Boutilier
• Utilities as Random Variables: Density Estimation and Structure Discovery/ Urszula Chajewska and Daphne Koller
• A Decision Theoretic Approach to Targeted Advertising/ Max Chickering and David Heckerman
• Stochastic Logic Programs: Sampling, Inference and Applications/ James Cussens
• Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Discrete and Continuous Variables/ Scott Davies and Andrew Moore
• Rao-Blackwellised Filtering for Dynamic Bayesian Networks/ Arnaud Doucet, Nando de Freitas, Kevin Murphy, and Stuart Russell
• Being Bayesian about Network Structure/ Nir Friedman and Daphne Koller
• Gaussian Process Networks/ Nir Friedman and Iftach Nachman
• A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs/ Phan Giang and Prakash P. Shenoy
• Building a Stochastic Dynamic Model of Application Use/ Peter Gorniak and David Poole
• Maximum Entropy and the Glasses You Are Looking Through/ Peter Grunwald
• Conditional Plausibility Measures and Bayesian Networks/ Joseph Halpern
• Inference for Belief Networks Using Coupling From the Past/ Michael Harvey and Radford Neal
• Dependency Networks for Density Estimation, Collaborative Filtering, and Data Visualization/ David Heckerman, Max Chickering, Chris Meek, Robert Rounthwaite, and Carl Kadie
• Probabilistic Arc Consistency: A Connection Between Constraint Reasoning and Probabilistic Reasoning/ Michael Horsch and Bill Havens
• Feature Selection and Dualities in Maximum Entropy Discrimination/ Tony Jebara and Tommi Jaakola
• Solution Algorithms for Factored MDPs/ Daphne Koller and Ron Parr

Middle Earth Eating Club

UAI Posters

• Game Networks/ Pierfrancesco La Mura
• Causal Mechanism-based Model Construction/ Tsai-Ching Lu, Marek Druzdzel, and Tze-Yun Leong
• Risk Agoras: Dialectical Argumentation for Scientific Reasoning/ Peter McBurney and Simon Parsons
• Probabilistic Models for Agents' Beliefs and Decisions/ Brian Milch and Daphne Koller
• PEGASUS: A Policy Search Method for Large MDPs and POMDPs/ Andrew Ng and Mike Jordan
• Representing and Solving Asymmetric Bayesian Decision Problems/ Thomas D. Nielsen and Finn Jensen
• Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example/ Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Jensen, and Uffe Kjaelig;rulff
• Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach/ David Pennock, Eric Horvitz, Steve Lawrence, and C. Lee Giles
• Compact Securities Markets for Pareto Optimal Reallocation of Risk/ David Pennock and Michael Wellman
• Learning to Cooperate via Policy Search/ Leonid Peshkin, Kee-Eung Kim, Nicolas Meuleau, and Leslie Kaelbling
• Value-Directed Belief State Approximation for POMDPs/ Pascal Poupart and Craig Boutilier
• Probabilistic State-Dependent Grammars for Plan Recognition/ David Pynadath and Michael Wellman
• Pivotal Pruning of Trade-offs in QPNs/ Silja Renooij, Linda C. van der Gaag, Simon Parsons, and Shaw Green
• Monte Carlo Inference via Greedy Importance Sampling/ D. Schuurmans and F. Southey
• Combining Feature and Prototype Pruning by Uncertainty Minimization/ Marc Sebban and Richard Nock
• Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules/ A. Storkey
• An Uncertainty Framework for Classification/ Loo-Nin Teow and Kia-Fock Loe
• A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks/ Jin Tian
• Probabilities of Causation: Bounds and Identification/ Jin Tian and Judea Pearl
• User Interface Tools for Navigation in Conditional Probability Tables and Graphical Elicitation of Probabilities in Bayesian Networks/ Haiqin Wang and Marek Druzdzel
• Variational Approximations between Mean Field Theory and the Junction Tree Algorithm/ Wim Wiegerinck
• Model Criticism of Bayesian Networks with Latent Variables/ David Williamson, Russell Almond, and Robert Mislevy
• Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks/ Frank Wittig and Anthony Jameson

Avanti Eating Club

Feature and Instance Selection

• Selection of Support Vector Kernel Parameters for Improved Generalization/ Loo-Nin Teow and Kia-Fock Loe
• Instance Pruning as an Information Preserving Problem/ Marc Sebban and Richard Nock
• Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning/ Mark A. Hall
• Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies/ Luis Talavera
• Feature Subset Selection and Order Identification for Unsupervised Learning/ Jennifer G. Dy and Carla E. Brodley

Feature Construction

• Induction of Concept Hierarchies from Noisy Data/ Blaz Zupan, Ivan Bratko, Marko Bohanec, and Janez Demsar
• Mutual Information in Learning Feature Transformations/ Kari Torkkola and William Campbell
• Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning/ Thomas R. Shultz and Francois Rivest
• Constructive Feature Learning and the Development of Visual Expertise/ Justus H. Piater and Roderic A. Grupen

Supervised Learning

• The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms/ Jinyan Li, Kotagiri Ramamohanarao, and Guozhu Dong
• Direct Bayes Point Machines/ Matthias Rychetsky, John Shawe-Taylor, and Manfred Glesner
• FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness/ Joseph O'Sullivan, John Langford, Rich Caruana, and Avrim Blum
• A Column Generation Algorithm For Boosting/ Kristin P. Bennett, Ayhan Demiriz, and John Shawe-Taylor
• Linear Discriminant Trees/ Olcay Taner Yildiz and Ethem Alpaydin
• Solving the Multiple-Instance Problem: A Lazy Learning Approach/ Jun Wang and Jean-Daniel Zucker
• Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers/ Erin L. Allwein, Robert E. Schapire, and Yoram Singer

Cost-Sensitive Learning

• A Comparative Study of Cost-Sensitive Boosting Algorithms/ Kai Ming Ting
• Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers/ Dragos D. Margineantu and Thomas G. Dietterich
• Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria/ Chris Drummond and Robert C. Holte

Beefeaters Eating Club

Adaptive User Interfaces

• Learning Subjective Functions with Large Margins/ Claude-Nicolas Fiechter and Seth Rogers
• Algorithms for Inverse Reinforcement Learning/ Andrew Y. Ng and Stuart Russell
• Clustering the Users of Large Web Sites into Communities/ Georgios Paliouras, Christos Papatheodorou, Vangelis Karkaletsis, and Constantine Spyropoulos
• Version Space Algebra and its Application to Programming by Demonstration/ Tessa Lau, Pedro Domingos, and Daniel S. Weld
• Incremental Learning in SwiftFile/ Richard B. Segal and Jeffrey O. Kephart
• Learning to Create Customized Authority Lists/ Huan Chang, David Cohn, and Andrew K. McCallum
• Challenges of the Email Domain for Text Classification/ Jake D. Brutlag and Christopher Meek
• State-based Classification of Finger Gestures from Electromyographic Signals/ Peter Ju, Leslie Pack Kaelbling, and Yoram Singer

Visual Learning

• Using Learning by Discovery to Segment Remotely Sensed Images/ Leen-Kiat Soh and Costas Tsatsoulis
• Discovering Homogeneous Regions in Spatial Data through Competition/ Slobodan Vucetic and Zoran Obradovic
• Image Color Constancy Using EM and Cached Statistics/ Charles Rosenberg
• An Initial Study of an Adaptive Hierarchical Vision System/ Marcus A. Maloof
• Learning Bayesian Networks for Diverse and Varying Numbers of Evidence Sets/ ZuWhan Kim and Ramakant Nevatia

Multi-agent Learning

• Combining Multiple Perspectives/ Bikramit Banerjee, Sandip Debnath, and Sandip Sen
• Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions/ Manu Sridharan and Gerald Tesauro
• Multi-Agent Reinforcement Learning for Traffic Light Control/ Marco Wiering
• TPOT-RL Applied to Network Routing/ Peter Stone
• An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems/ Martin Lauer and Martin Riedmiller
• Pseudo-convergent Q-Learning by Competitive Pricebots/ Jeffrey O. Kephart and Gerald J. Tesauro
• Convergence Problems of General-Sum Multiagent Reinforcement Learning/ Michael Bowling
• Experimental Results on Q-Learning for General-Sum Stochastic Games/ Junling Hu and Michael P. Wellman

Bollard Eating Club

Learning in Problem Solving and Planning

• Knowledge Representation Issues in Control Knowledge Learning/ Ricardo Aler, Daniel Borrajo, and Pedro Isasi
• Learning Declarative Control Rules for Constraint-Based Planning/ Yi-Cheng Huang, Bart Selman, and Henry Kautz
• Machine Learning for Subproblem Selection/ Robert Moll, Theodore J. Perkins, and Andrew G. Barto
• Achieving Efficient and Cognitively Plausible Learning in Backgammon/ Scott Sanner, John R. Anderson, Christian Lebiere, and Marsha Lovett
• Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots/ Daniel Nikovski and Illah Nourbakhsh
• Learning Through Evolution: Neural Programming and Internal Reinforcement/ Astro Teller and Manuela Veloso
• Reinforcement Learning in POMDP's via Direct Gradient Ascent/ Jonathan Baxter and Peter L. Bartlett
• Algorithm Selection using Reinforcement Learning/ Michail Lagoudakis and Michael Littman

Analyses and Rates of Reinforcement Learning

• Fixed Points of Approximate Value Iteration and Temporal-Difference Learning/ Daniela Pucci de Farias and Benjamin Van Roy
• Relative Loss Bounds for Temporal-Difference Learning/ Jurgen Forster and Manfred Warmuth
• A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions/ Susumu Katayama, Hajime Kimura, and Shigenobu Kobayashi
• Eligibility Traces for Off-Policy Policy Evaluation/ Doina Precup, Richard S. Sutton, and Satinder Singh
• Knowledge Propagation in Model-based Reinforcement Learning Tasks/ Corinna Richter and Jorg Stachowiak
• Shaping in Reinforcement Learning by Changing the Physics of the Problem/ Jette Randlov
• Localizing Policy Gradient Estimates to Action Transitions/ Gregory Z. Grudic and Lyle H. Ungar
• A Bayesian Framework for Reinforcement Learning/ Malcolm Strens

Active Learning and Dealing with Noise

• Less is More: Active Learning with Support Vector Machines/ Greg Schohn and David Cohn
• Efficient Mining from Large Databases by Query Learning/ Hiroshi Mamitsuka and Naoki Abe
• Query Learning with Large Margin Classifiers/ Colin Campbell, Nello Cristianini, and Alex Smola
• Learning Priorities From Noisy Examples/ Geoffrey G. Towell, Thomas Petsche, and Michael R. Miller
• A Nonparametric Approach to Noisy and Costly Optimization/ Brigham S. Anderson, Andrew W. Moore, and David Cohn

Middle Earth Eating Club

Natural Language Learning

• Discriminative Reranking for Natural Language Parsing/ Michael Collins
• Maximum Entropy Markov Models for Information Extraction and Segmentation/ Andrew McCallum, Dayne Freitag, and Fernando Pereira
• Meta-Learning for Phonemic Annotation of Corpora/ Veronique Hoste, Walter Daelemans, Erik Tjong Kim Sang, and Steven Gillis
• Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning/ Antal van den Bosch and Jakub Zavrel
• Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval/ Keith Hall and Thomas Hofmann
• Using Natural Language Processing and Discourse Features to Identify Understanding Errors in a Spoken Dialogue System/ Marilyn Walker, Jerry Wright, and Irene Langkilde
• Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse/ Tadashi Nomoto and Yuji Matsumoto
• A Boosting Approach to Topic Spotting on Subdialogues/ Kary Myers, Michael Kearns, Satinder Singh, and Marilyn A. Walker
• Bootstrapping Syntax and Recursion using Alignment-Based Learning/ Menno van Zaanen
• An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata/ Stefan Veeser
• Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality/ Franck Thollard, Pierre Dupont, and Colin de la Higuera

Scientific Applications of Machine Learning

• Learning Filaments/ Geoffrey J. Gordon and Andrew Moore
• Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes/ Mark Craven, David Page, Jude Shavlik, Joseph Bockhorst, and Jeremy Glasner
• Learning Chomsky-like Grammars for Biological Sequence Families/ S. H. Muggleton, C. H. Bryant, and A. Srinivasan

Semi-supervised and Unsupervised Learning

• Clustering with Instance-level Constraints/ Kiri Wagstaff and Claire Cardie
• Hierarchical Unsupervised Learning/ Shivakumar Vaithyanathan and Byron Dom
• A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models/ Cen Li and Gautam Biswas
• Mixtures of Factor Analyzers/ Geoffrey McLachlan and David Peel
• Discovering Test Set Regularities in Relational Domains/ Sean Slattery and Tom Mitchell
• A Probability Analysis on the Value of Unlabeled Data for Classification Problems/ Tong Zhang and Frank J. Oles
• An Adaptive Regularization Criterion for Supervised Learning/ Dale Schuurmans and Finnegan Southey
• Enhancing Supervised Learning with Unlabeled Data/ Sally Goldman and Yan Zhou