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 1996, ISBN 1-55860-419-7 
Contributed Papers
- Naoki Abe, Hang Li:
Learning Word Association Norms Using Tree Cut Pair Models.
3-11
- Aynur Akkus, H. Altay Güvenir:
K Nearest Neighbor Classification on Feature Projections.
12-19
- Cesar Bandera, Francisco J. Vico, José Manuel Bravo, Mance E. Harmon, Leemon C. Baird III:
Residual Q-Learning Applied to Visual Attention.
20-27
- Eric B. Baum:
Toward a Model of Mind as a Laissez-Faire Economy of Idiots.
28-36
- Enrico Blanzieri, Patrick 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
- Craig Boutilier, Richard Dearden:
Approximate Value Trees in Structured Dynamic Programming.
54-62
- Justin A. Boyan, Andrew W. Moore:
Learning Evaluation Functions for Large Acyclic Domains.
63-70
- Christopher J. C. Burges:
Simplified Support Vector Decision Rules.
71-77
- Leonardo Carbonara, Derek H. Sleeman:
Improving the Efficiency of Knowledge Base Refinement.
78-86
- Rich Caruana:
Algorithms and Applications for Multitask Learning.
87-95
- Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour:
Applying the Waek Learning Framework to Understand and Improve C4.5.
96-104
- Pedro Domingos, Michael J. Pazzani:
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier.
105-112
- Steven K. Donoho, Larry A. Rendell:
Constructive Induction Using Fragmentary Knowledge.
113-121
- Werner Emde, Dietrich Wettschereck:
Relational Instance-Based Learning.
122-130
- Sean P. Engelson, Moshe Koppel:
Identifying the Information Contained in a Flawed Theory.
131-138
- Kazuo J. Ezawa, Moninder Singh, Steven W. Norton:
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management.
139-147
- Yoav Freund, Robert E. Schapire:
Experiments with a New Boosting Algorithm.
148-156
- Nir Friedman, Moisés Goldszmidt:
Discretizing Continuous Attributes While Learning Bayesian Networks.
157-165
- Peter Geibel, Fritz Wysotzki:
Learning Relational Concepts with Decision Trees.
166-174
- Patrick Goetz, Shailesh Kumar, Risto Miikkulainen:
On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning.
175-181
- Andrew R. Golding, Dan Roth:
Applying Winnow to Context-Sensitive Spelling Correction.
182-190
- Sally A. Goldman, Stephen D. Scott:
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns.
191-199
- Geoffrey J. Gordon, Alberto Maria Segre:
Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning.
200-206
- Russell Greiner, Adam J. Grove, Dan Roth:
Learning Active Classifiers.
207-215
- Russell Greiner, Adam J. Grove, Alexander Kogan:
Exploiting the Omission of Irrelevant Data.
216-224
- Stephan Grolimund, Jean-Gabriel Ganascia:
Speeding-up Nearest Neighbour Memories: The Template Tree Case Memory Organisation.
225-233
- Jukka Hekanaho:
Background Knowledge in GA-based Concept Learning.
234-242
- David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth:
On-Line Portfolio Selection Using Multiplicative Updates.
243-251
- Andreas Ittner, Michael Schlosser:
Non-Linear Decision Trees - NDT.
252-257
- Pascal Jappy, Richard Nock, Olivier Gascuel:
Negative Robust Learning Results from Horn Claus Programs.
258-265
- Sven Koenig, Reid G. Simmons:
Passive Distance Learning for Robot Navigation.
266-274
- Ron Kohavi, David Wolpert:
Bias Plus Variance Decomposition for Zero-One Loss Functions.
275-283
- Daphne Koller, Mehran Sahami:
Toward Optimal Feature Selection.
284-292
- Miroslav Kubat:
Second Tier for Decision Trees.
293-301
- Richard H. Lathrop:
On the Learnability of the Uncomputable.
302-309
- Michael L. Littman, Csaba Szepesvári:
A Generalized Reinforcement-Learning Model: Convergence and Applications.
310-318
- Huan Liu, Rudy Setiono:
A Probabilistic Approach to Feature Selection - A Filter Solution.
319-327
- Sridhar Mahadevan:
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning.
328-336
- Rémi Munos:
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning.
337-345
- Tim Oates, Paul R. Cohen:
Searching for Structure in Multiple Streams of Data.
346-354
- Seishi Okamoto, Nobuhiro Yugami:
Theoretical Analysis of the Nearest Neighbor Classifier in Noisy Domains.
355-363
- Jonathan J. Oliver, Rohan A. Baxter, Chris S. Wallace:
Unsupervised Learning Using MML.
364-372
- Mark D. Pendrith, Malcolm R. K. Ryan:
Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results.
373-381
- M. Alicia Pérez:
Representing and Learning Quality-Improving Search Control Knowledge.
382-390
- Eduardo Pérez, Larry A. Rendell:
Learning Despite Concept Variation by Finding Structure in Attribute-based Data.
391-399
- Caroline Ravise, Michèle Sebag:
An Advanced Evolution Should Not Repeat its Past Errors.
400-408
- Chandra Reddy, Prasad Tadepalli, Silvana Roncagliolo:
Theory-guided Empirical Speedup Learning of Goal Decomposition Rules.
409-417
- Davide Roverso:
Analogy Access by Mapping Spreading and Abstraction in Large, Multifunctional Knowledge Bases.
418-426
- Marco Saerens:
Non Mean Square Error Criteria for the Training of Learning Machines.
427-434
- Mehran Sahami, Marti A. Hearst, Eric Saund:
Applying the Multiple Cause Mixture Model to Text Categorization.
435-443
- Michèle Sebag:
Delaying the Choice of Bias: A Disjunctive Version Space Approach.
444-452
- Moninder Singh, Gregory M. Provan:
Efficient Learning of Selective Bayesian Network Classifiers.
453-461
- Joe Suzuki:
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique.
462-470
- Prasad Tadepalli, DoKyeong Ok:
Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function.
471-479
- Kang Soo Tae, Diane J. Cook:
Experimental Knowledge Acquisition for Planning.
480-488
- Sebastian Thrun, Joseph O'Sullivan:
Discovering Structure in Multiple Learning Tasks: The TC Algorithm.
489-497
- Kai Ming Ting:
The Characterisation of Predictive Accuracy and Decision Combination.
498-506
- Henry Tirri, Petri Kontkanen, Petri Myllymäki:
Prababilistic Instance-Based Learning.
507-515
- Chris S. Wallace, Kevin B. Korb, Honghua Dai:
Causal Discovery via MML.
516-524
- Gerhard Widmer:
Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning.
525-533
- Marco Wiering, Jürgen Schmidhuber:
Solving POMDPs with Levin Search and EIRA.
534-542
- Jean-Daniel Zucker, Jean-Gabriel Ganascia:
Representation Changes for Efficient Learning in Structural Domains.
543-551
Invited Talks
- Heikki Mannila:
Data Mining and Machine Learning (Abstract).
555
- Andrew W. Moore:
Reinforcement Learning in Factories: The Auton Project (Abstract).
556
- Vladimir Vapnik:
Statistical Theory of Generalization (Abstract).
557
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