icml 1997 论文列表Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), Nashville, Tennessee, USA, July 8-12, 1997. |
Machine Learning by Function Decomposition.
A Comparative Study on Feature Selection in Text Categorization.
Instance Pruning Techniques.
Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction.
Functional Models for Regression Tree Leaves.
Declarative Bias in Equation Discovery.
Stacking Bagged and Dagged Models.
Hierarchical Explanation-Based Reinforcement Learning.
A Bayesian Approach to Model Learning in Non-Markovian Environments.
Characterizing the generalization performance of model selection strategies.
Why Experimentation can be better than "Perfect Guidance".
Boosting the margin: A new explanation for the effectiveness of voting methods.
Using output codes to boost multiclass learning problems.
Predicting Multiprocessor Memory Access Patterns with Learning Models.
An adaptation of Relief for attribute estimation in regression.
Learning String Edit Distance.
Learning Goal-Decomposition Rules using Exercises.
Exponentiated Gradient Methods for Reinforcement Learning.
The Effective Size of a Neural Network: A Principal Component Approach.
The Effects of Training Set Size on Decision Tree Complexity.
Preventing "Overfitting" of Cross-Validation Data.
Efficient Locally Weighted Polynomial Regression Predictions.
ARCCHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods.
On the Decomposition of Polychotomies into Dichotomies.
Pruning Adaptive Boosting.
Pessimistic decision tree pruning based Continuous-time.
Automatic Rule Acquisition for Spelling Correction.
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection.
Hierarchically Classifying Documents Using Very Few Words.
Option Decision Trees with Majority Votes.
Reinforcement Learning in POMDPs with Function Approximation.
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization.
Probabilistic Linear Tree.
Learning Belief Networks in the Presence of Missing Values and Hidden Variables.
Expected Mistake Bound Model for On-Line Reinforcement Learning.
Improving Regressors using Boosting Techniques.
Knowledge Acquisition form Examples Vis Multiple Models.
Efficient Feature Selection in Conceptual Clustering.
PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction.
Learning Symbolic Prototypes.
A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction.
Improving Minority Class Prediction Using Case-Specific Feature Weights.
FONN: Combining First Order Logic with Connectionist Learning.
The Canonical Distortion Measure for Vector Quantization and Function Approximation.
Using Optimal Dependency-Trees for Combinational Optimization.
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach.
Robot Learning From Demonstration.
Feature Engineering and Classifier Selection: A Case Study in Venusian Volcano Detection.