icml 2001 论文列表Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001. |
Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning.
Some Sparse Approximation Bounds for Regression Problems.
Learnability of Augmented Naive Bayes in Nonimal Domains.
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers.
Feature selection for high-dimensional genomic microarray data.
Exploration Control in Reinforcement Learning using Optimistic Model Selection.
Reinforcement Learning in Dynamic Environments using Instantiated Information.
Constrained K-means Clustering with Background Knowledge.
A procedure for unsupervised lexicon learning.
Improving Probabilistic Grammatical Inference Core Algorithms with Post-processing Techniques.
A Multi-Agent Policy-Gradient Approach to Network Routing.
Direct Policy Search using Paired Statistical Tests.
Scaling Reinforcement Learning toward RoboCup Soccer.
Learning to Generate Fast Signal Processing Implementations.
Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation.
Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov Sources.
Boosting Neighborhood-Based Classifiers.
Clustering Continuous Time Series.
Discovering Communicable Scientific Knowledge from Spatio-Temporal Data.
Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems.
Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems.
Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time Series.
Repairing Faulty Mixture Models using Density Estimation.
Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant Attributes.
Toward Optimal Active Learning through Sampling Estimation of Error Reduction.
Comprehensible Interpretation of Relief's Estimates.
Multiple Instance Regression.
Off-Policy Temporal Difference Learning with Function Approximation.
Lyapunov-Constrained Action Sets for Reinforcement Learning.
Mixtures of Rectangles: Interpretable Soft Clustering.
Breeding Decision Trees Using Evolutionary Techniques.
Ridge Regression Confidence Machine.
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection.
Some Greedy Algorithms for Sparse Nonlinear Regression.
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density.
Coupled Clustering: a Method for Detecting Structural Correspondence.
Learning with the Set Covering Machine.
Inducing Partially-Defined Instances with Evolutionary Algorithms.
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots.
Friend-or-Foe Q-learning in General-Sum Games.
Collaborative Learning and Recommender Systems.
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise.
Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensing.
An Improved Predictive Accuracy Bound for Averaging Classifiers.
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
Boosting Noisy Data.
Pairwise Comparison of Hypotheses in Evolutionary Learning.
Feature Construction with Version Spaces for Biochemical Applications.
Composite Kernels for Hypertext Categorisation.
Learning to Select Good Title Words: An New Approach based on Reverse Information Retrieval.
Some Theoretical Aspects of Boosting in the Presence of Noisy Data.
On No-Regret Learning, Fictitious Play, and Nash Equilibrium.
Expectation Maximization for Weakly Labeled Data.
General Loss Bounds for Universal Sequence Prediction.
Bayesian approaches to failure prediction for disk drives.
Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State.
Continuous-Time Hierarchical Reinforcement Learning.
Hypertext Categorization using Hyperlink Patterns and Meta Data.
Learning Probabilistic Models of Relational Structure.
Reinforcement Learning with Bounded Risk.
WBCsvm: Weighted Bayesian Classification based on Support Vector Machines.
Round Robin Rule Learning.
Learning Embedded Maps of Markov Processes.
A Theory-Refinement Approach to Information Extraction.
Relevance Feedback using Support Vector Machines.
Visual Development and the Acquisition of Binocular Disparity Sensitivities.
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering.
An Efficient Approach for Approximating Multi-dimensional Range Queries and Nearest Neighbor Classification in Large Datasets.
Bias Correction in Classification Tree Construction.
Structured Prioritised Sweeping.
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection.
Latent Semantic Kernels.
Boosting with Confidence Information.
A Unified Loss Function in Bayesian Framework for Support Vector Regression.
A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning.
Learning an Agent's Utility Function by Observing Behavior.
Convergence of Gradient Dynamics with a Variable Learning Rate.
Learning from Labeled and Unlabeled Data using Graph Mincuts.
Efficient algorithms for decision tree cross-validation.
Multiple-Instance Learning of Real-Valued Data.