13. ICML 1996:
Bari,
Italy
Lorenza Saitta (Ed.):
Machine Learning,
Proceedings of the Thirteenth International Conference (ICML '96),
Bari,
Italy,
July 3-6,
1996. Morgan Kaufmann,
ISBN 1-55860-419-7
Contributed Papers
- Naoki Abe, Hang Li:
Learning Word Association Norms Using Tree Cut Pair Models.
3-11 BibTeX
- Aynur Akkus, H. Altay Güvenir:
K Nearest Neighbor Classification on Feature Projections.
12-19 BibTeX
- Cesar Bandera, Francisco J. Vico, José Manuel Bravo, Mance E. Harmon, Leemon C. Baird III:
Residual Q-Learning Applied to Visual Attention.
20-27 BibTeX
- Eric B. Baum:
Toward a Model of Mind as a Laissez-Faire Economy of Idiots.
28-36 BibTeX
E. Blanzieri,
P. Katenkamp:
Learning Radial Basis Function Networks On-line. 37-45
- Henrik Boström:
Theory-Guideed Induction of Logic Programs by Inference of Regular Languages.
46-53 BibTeX
- Craig Boutilier, Richard Dearden:
Approximate Value Trees in Structured Dynamic Programming.
54-62 BibTeX
- Justin A. Boyan, Andrew W. Moore:
Learning Evaluation Functions for Large Acyclic Domains.
63-70 BibTeX
- Christopher J. C. Burges:
Simplified Support Vector Decision Rules.
71-77 BibTeX
- Leonardo Carbonara, Derek H. Sleeman:
Improving the Efficiency of Knowledge Base Refinement.
78-86 BibTeX
- Rich Caruana:
Algorithms and Applications for Multitask Learning.
87-95 BibTeX
- Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour:
Applying the Waek Learning Framework to Understand and Improve C4.5.
96-104 BibTeX
- Pedro Domingos, Michael J. Pazzani:
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier.
105-112 BibTeX
- Steven K. Donoho, Larry A. Rendell:
Constructive Induction Using Fragmentary Knowledge.
113-121 BibTeX
- Werner Emde, Dietrich Wettschereck:
Relational Instance-Based Learning.
122-130 BibTeX
- Sean P. Engelson, Moshe Koppel:
Identifying the Information Contained in a Flawed Theory.
131-138 BibTeX
- Kazuo J. Ezawa, Moninder Singh, Steven W. Norton:
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management.
139-147 BibTeX
- Yoav Freund, Robert E. Schapire:
Experiments with a New Boosting Algorithm.
148-156 BibTeX
- Nir Friedman, Moisés Goldszmidt:
Discretizing Continuous Attributes While Learning Bayesian Networks.
157-165 BibTeX
- Peter Geibel, Fritz Wysotzki:
Learning Relational Concepts with Decision Trees.
166-174 BibTeX
- Patrick Goetz, Shailesh Kumar, Risto Miikkulainen:
On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning.
175-181 BibTeX
- Andrew R. Golding, Dan Roth:
Applying Winnow to Context-Sensitive Spelling Correction.
182-190 BibTeX
- Sally A. Goldman, Stephen D. Scott:
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns.
191-199 BibTeX
- Geoffrey J. Gordon, Alberto Maria Segre:
Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning.
200-206 BibTeX
- Russell Greiner, Adam J. Grove, Dan Roth:
Learning Active Classifiers.
207-215 BibTeX
- Russell Greiner, Adam J. Grove, Alexander Kogan:
Exploiting the Omission of Irrelevant Data.
216-224 BibTeX
- Stephan Grolimund, Jean-Gabriel Ganascia:
Speeding-up Nearest Neighbour Memories: The Template Tree Case Memory Organisation.
225-233 BibTeX
- Jukka Hekanaho:
Background Knowledge in GA-based Concept Learning.
234-242 BibTeX
- David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth:
On-Line Portfolio Selection Using Multiplicative Updates.
243-251 BibTeX
- Andreas Ittner, Michael Schlosser:
Non-Linear Decision Trees - NDT.
252-257 BibTeX
- Pascal Jappy, Richard Nock, Olivier Gascuel:
Negative Robust Learning Results from Horn Claus Programs.
258-265 BibTeX
- Sven Koenig, Reid G. Simmons:
Passive Distance Learning for Robot Navigation.
266-274 BibTeX
- Ron Kohavi, David Wolpert:
Bias Plus Variance Decomposition for Zero-One Loss Functions.
275-283 BibTeX
- Daphne Koller, Mehran Sahami:
Toward Optimal Feature Selection.
284-292 BibTeX
- Miroslav Kubat:
Second Tier for Decision Trees.
293-301 BibTeX
- Richard H. Lathrop:
On the Learnability of the Uncomputable.
302-309 BibTeX
- Michael L. Littman, Csaba Szepesvári:
A Generalized Reinforcement-Learning Model: Convergence and Applications.
310-318 BibTeX
- Huan Liu, Rudy Setiono:
A Probabilistic Approach to Feature Selection - A Filter Solution.
319-327 BibTeX
- Sridhar Mahadevan:
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning.
328-336 BibTeX
- Rémi Munos:
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning.
337-345 BibTeX
- Tim Oates, Paul R. Cohen:
Searching for Structure in Multiple Streams of Data.
346-354 BibTeX
- Seishi Okamoto, Nobuhiro Yugami:
Theoretical Analysis of the Nearest Neighbor Classifier in Noisy Domains.
355-363 BibTeX
- Jonathan J. Oliver, Rohan A. Baxter, Chris S. Wallace:
Unsupervised Learning Using MML.
364-372 BibTeX
- Mark D. Pendrith, Malcolm R. K. Ryan:
Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results.
373-381 BibTeX
- M. Alicia Pérez:
Representing and Learning Quality-Improving Search Control Knowledge.
382-390 BibTeX
- Eduardo Pérez, Larry A. Rendell:
Learning Despite Concept Variation by Finding Structure in Attribute-based Data.
391-399 BibTeX
- Caroline Ravise, Michèle Sebag:
An Advanced Evolution Should Not Repeat its Past Errors.
400-408 BibTeX
- Chandra Reddy, Prasad Tadepalli, Silvana Roncagliolo:
Theory-guided Empirical Speedup Learning of Goal Decomposition Rules.
409-417 BibTeX
- Davide Roverso:
Analogy Access by Mapping Spreading and Abstraction in Large, Multifunctional Knowledge Bases.
418-426 BibTeX
- Marco Saerens:
Non Mean Square Error Criteria for the Training of Learning Machines.
427-434 BibTeX
- Mehran Sahami, Marti A. Hearst, Eric Saund:
Applying the Multiple Cause Mixture Model to Text Categorization.
435-443 BibTeX
- Michèle Sebag:
Delaying the Choice of Bias: A Disjunctive Version Space Approach.
444-452 BibTeX
- Moninder Singh, Gregory M. Provan:
Efficient Learning of Selective Bayesian Network Classifiers.
453-461 BibTeX
- Joe Suzuki:
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique.
462-470 BibTeX
- Prasad Tadepalli, DoKyeong Ok:
Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function.
471-479 BibTeX
- Kang Soo Tae, Diane J. Cook:
Experimental Knowledge Acquisition for Planning.
480-488 BibTeX
- Sebastian Thrun, Joseph O'Sullivan:
Discovering Structure in Multiple Learning Tasks: The TC Algorithm.
489-497 BibTeX
- Kai Ming Ting:
The Characterisation of Predictive Accuracy and Decision Combination.
498-506 BibTeX
- Henry Tirri, Petri Kontkanen, Petri Myllymäki:
Prababilistic Instance-Based Learning.
507-515 BibTeX
- Chris S. Wallace, Kevin B. Korb, Honghua Dai:
Causal Discovery via MML.
516-524 BibTeX
- Gerhard Widmer:
Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning.
525-533 BibTeX
- Marco Wiering, Jürgen Schmidhuber:
Solving POMDPs with Levin Search and EIRA.
534-542 BibTeX
- Jean-Daniel Zucker, Jean-Gabriel Ganascia:
Representation Changes for Efficient Learning in Structural Domains.
543-551 BibTeX
Invited Talks
Copyright © Sat May 16 23:20:39 2009
by Michael Ley (ley@uni-trier.de)
