icml 1999 论文列表Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999. |
A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning Algorithms.
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees.
An Region-Based Learning Approach to Discovering Temporal Structures in Data.
Large Margin Trees for Induction and Transduction.
Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes.
Learning Comprehensible Descriptions of Multivariate Time Series.
Machine-Learning Applications of Algorithmic Randomness.
Model Selection in Unsupervised Learning with Applications To Document Clustering.
Approximation Via Value Unification.
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes.
Active Learning for Natural Language Parsing and Information Extraction.
OPT-KD: An Algorithm for Optimizing Kd-Trees.
Feature Selection as a Preprocessing Step for Hierarchical Clustering.
Feature Engineering for Text Classification.
Distributed Value Functions.
Expected Error Analysis for Model Selection.
GA-based Learning of Context-Free Grammars using Tabular Representations.
Attribute Dependencies, Understandability and Split Selection in Tree Based Models.
Using Reinforcement Learning to Spider the Web Efficiently.
Implicit Imitation in Multiagent Reinforcement Learning.
Noise-Tolerant Recursive Best-First Induction.
Learning Policies with External Memory.
Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples.
Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System.
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping.
Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring.
Feature Selection for Unbalanced Class Distribution and Naive Bayes.
An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data.
Correcting Noisy Data.
Learning Hierarchical Performance Knowledge by Observation.
Tractable Average-Case Analysis of Naive Bayesian Classifiers.
Efficient Non-Linear Control by Combining Q-learning with Local Linear Controllers.
Transductive Inference for Text Classification using Support Vector Machines.
Distributed Robotic Learning: Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real World.
Detecting Motifs from Sequences.
Boosting a Strong Learner: Evidence Against the Minimum Margin.
On Some Misbehaviour of Back-Propagation with Non-Normalized RBFNs and a Solution.
Learning User Evaluation Functions for Adaptive Scheduling Assistance.
Experiments with Noise Filtering in a Medical Domain.
Discriminant Trees.
The Alternating Decision Tree Learning Algorithm.
Making Better Use of Global Discretization.
Abstracting from Robot Sensor Data using Hidden Markov Models.
AdaCost: Misclassification Cost-Sensitive Boosting.
Combining Error-Driven Pruning and Classification for Partial Parsing.
Hierarchical Models for Screening of Iron Deficiency Anemia.
Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM.
Learning to Ride a Bicycle using Iterated Phantom Induction.
Least-Squares Temporal Difference Learning.
Instance-Family Abstraction in Memory-Based Language Learning.
Local Learning for Iterated Time-Series Prediction.
A Minimum Risk Metric for Nearest Neighbor Classification.
Learning to Optimally Schedule Internet Banner Advertisements.
Associative Reinforcement Learning using Linear Probabilistic Concepts.