14. ICML 1997:
Nashville, TN, USA
Douglas H. Fisher (Ed.):
Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), Nashville, Tennessee, USA, July 8-12, 1997.
Morgan Kaufmann 1997, ISBN 1-55860-486-3 
- Lars Asker, Richard Maclin:
Feature Engineering and Classifier Selection: A Case Study in Venusian Volcano Detection.
3-11
- Christopher G. Atkeson, Stefan Schaal:
Robot Learning From Demonstration.
12-20
- Peter Auer:
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach.
21-29
- Shumeet Baluja, Scott Davies:
Using Optimal Dependency-Trees for Combinational Optimization.
30-38
- Jonathan Baxter:
The Canonical Distortion Measure for Vector Quantization and Function Approximation.
39-47
- Marco Botta, Attilio Giordana, Roberto Piola:
FONN: Combining First Order Logic with Connectionist Learning.
46-56
- Claire Cardie, Nicholas Nowe:
Improving Minority Class Prediction Using Case-Specific Feature Weights.
57-65
- William W. Cohen, Premkumar T. Devanbu:
A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction.
66-74
- Piew Datta, Dennis F. Kibler:
Learning Symbolic Prototypes.
75-82
- Scott E. Decatur:
PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction.
83-91
- Mark Devaney, Ashwin Ram:
Efficient Feature Selection in Conceptual Clustering.
92-97
- Pedro Domingos:
Knowledge Acquisition form Examples Vis Multiple Models.
98-106
- Harris Drucker:
Improving Regressors using Boosting Techniques.
107-115
- Claude-Nicolas Fiechter:
Expected Mistake Bound Model for On-Line Reinforcement Learning.
116-124
- Nir Friedman:
Learning Belief Networks in the Presence of Missing Values and Hidden Variables.
125-133
- Joao Gama:
Probabilistic Linear Tree.
134-142
- Thorsten Joachims:
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization.
143-151
- Hajime Kimura, Kazuteru Miyazaki, Shigenobu Kobayashi:
Reinforcement Learning in POMDPs with Function Approximation.
152-160
- Ron Kohavi, Clayton Kunz:
Option Decision Trees with Majority Votes.
161-169
- Daphne Koller, Mehran Sahami:
Hierarchically Classifying Documents Using Very Few Words.
170-178
- Miroslav Kubat, Stan Matwin:
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection.
179-186
- Lidia Mangu, Eric Brill:
Automatic Rule Acquisition for Spelling Correction.
187-194
- Yishay Mansour:
Pessimistic decision tree pruning based Continuous-time.
202-210
- Dragos D. Margineantu, Thomas G. Dietterich:
Pruning Adaptive Boosting.
211-218
- Eddy Mayoraz, Miguel Moreira:
On the Decomposition of Polychotomies into Dichotomies.
219-226
- Filippo Menczer:
ARCCHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods.
227-235
- Andrew W. Moore, Jeff G. Schneider, Kan Deng:
Efficient Locally Weighted Polynomial Regression Predictions.
236-244
- Andrew Y. Ng:
Preventing "Overfitting" of Cross-Validation Data.
245-253
- Tim Oates, David Jensen:
The Effects of Training Set Size on Decision Tree Complexity.
254-262
- David W. Opitz:
The Effective Size of a Neural Network: A Principal Component Approach.
263-271
- Doina Precup, Richard S. Sutton:
Exponentiated Gradient Methods for Reinforcement Learning.
272-277
- Chandra Reddy, Prasad Tadepalli:
Learning Goal-Decomposition Rules using Exercises.
278-286
- Eric Sven Ristad, Peter N. Yianilos:
Learning String Edit Distance.
287-295
- Marko Robnik-Sikonja, Igor Kononenko:
An adaptation of Relief for attribute estimation in regression.
296-304
- M. F. Sakr, Steven P. Levitan, Donald M. Chiarulli, Bill G. Horne, C. Lee Giles:
Predicting Multiprocessor Memory Access Patterns with Learning Models.
305-312
- Robert E. Schapire:
Using output codes to boost multiclass learning problems.
313-321
- Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee:
Boosting the margin: A new explanation for the effectiveness of voting methods.
322-330
- Tobias Scheffer, Russell Greiner, Christian Darken:
Why Experimentation can be better than "Perfect Guidance".
331-339
- Dale Schuurmans, Lyle H. Ungar, Dean P. Foster:
Characterizing the generalization performance of model selection strategies.
340-348
- Nobuo Suematsu, Akira Hayashi, Shigang Li:
A Bayesian Approach to Model Learning in Non-Markovian Environments.
349-357
- Prasad Tadepalli, Thomas G. Dietterich:
Hierarchical Explanation-Based Reinforcement Learning.
358-366
- Kai Ming Ting, Ian H. Witten:
Stacking Bagged and Dagged Models.
367-375
- Ljupco Todorovski, Saso Dzeroski:
Declarative Bias in Equation Discovery.
376-384
- Luís Torgo:
Functional Models for Regression Tree Leaves.
385-393
- Ricardo Vilalta, Larry A. Rendell:
Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction.
394-402
- D. Randall Wilson, Tony R. Martinez:
Instance Pruning Techniques.
403-411
- Yiming Yang, Jan O. Pedersen:
A Comparative Study on Feature Selection in Text Categorization.
412-420
- Blaz Zupan, Marko Bohanec, Ivan Bratko, Janez Demsar:
Machine Learning by Function Decomposition.
421-429
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