Poster Sessions / Thursday, June 29, 7:00 PM--10:00 PM
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
Poster Sessions / Friday, June 30, 7:00 PM--10:00 PM
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
Poster Sessions / Saturday, July 1, 7:00 PM--10:00 PM
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