icml 1991 论文列表Proceedings of the Eighth International Workshop (ML91), Northwestern University, Evanston, Illinois, USA. |
Knowledge Acquisition Combining Analytical and Empirrcal Techniques.
Decision Tree Induction of 3-D Manufacturing Features.
AIMS: An Adaptive Interactive Modeling System for Supporting Engineering Decision Making.
Database Consistency via Inductive Learning.
Design Integrated Learning Systems for Engineering Design.
Knowledge-Based Equation Discovery in Engineering Domains.
Improving Recognition Effectiveness of Noisy Texture Concepts.
Machine Learning for Nondestructive Evaluation.
Continous Conceptual Set Covering: Learning Robot Operators From Examples.
Learning Analytical Knowledge About VLSI-Design from Observation.
Model Revision: A Theory of Incremental Model Learning.
Knowledge Compilation to Speed Up Numerical Optimization.
Megainduction: A Test Flight.
Conceptual Clustering and Exploratory Data Analysis.
Comparing Stochastic Planning to the Acquisition of Increasingly Permissive Plans.
Noise-Resistant Classification.
Machine Learning in Engineering Automation.
Steve A. Chien Bradley L. Whitehall Thomas G. Dietterich Richard J. Doyle Brian Falkenhainer James Garrett Stephen C. Y. Lu
Identifying Cost Effective Boundaries of Operationality.
Is it a Pocket or a Purse? Tighly Coupled Theory and Data Driven Learing.
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems.
Using Background Knowledge in Concept Formation.
A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications.
Learning with Incrutable Theories.
Discovering Regularities from Large Knowledge Bases.
Improving Shared Rules in Multiple Category Domain Theories.
A Smallest Generalization Step Strategy.
Refining Domain Theories Expressed as Finite-State Automata.
Revision of Reduced Theories.
Revision Cost for Theory Refinement.
A Hybrid Approach to Guaranteed Effective Control Strategies.
An Enhancer for Reactive Plans.
Incremental Refinement of Approximate Domain Theories.
Probabilistic Evaluating of Bias for Learning Systems.
The Generality of Overgenerality.
The DUCTOR: A Theory Revision System for Propositional Domains.
Improving Learning Using Causality and Abduction.
Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning.
Learning Stochastic Motifs from Genetic Sequences.
Revising Relational Domain Theories.
Constraints on Predicate Invention.
Completeness for Inductive Procedures.
First-Order Theory Revision.
Determinate Literals in Inductive Logic Programming.
The Consistent Concept Axiom.
A Knowledge-intensive Approach to Learning Relational Concepts.
Learning Constrained Atoms.
Learning Search Control Rules for Planning: An Inductive Approach.
Efficient Learning of Logic Programs with Non-determinant, Non-discriminating Literals.
Using Inverse Resolution to Learn Relations from Experiments.
Learning Spatial Relations from Images.
Inducing Temporal Fault Diagnostic Rules from a Qualitative Model.
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL.
Integrity Constraints and Interactive Concept-Learning.
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms.
Learning Qualitative Models of Dynamic Systems.
Experiments in Non-Monotonic Learning.
Probabilistic Concept Formation in Relational Domains.
Scaling Reinforcement Learning Techniques via Modularity.
Complexity and Cooperation in Q-Learning.
Learning a Cost-Sensitive Internal Representation for Reinforcement Learning.
Planning by Incremental Dynamic Programming.
Transfer of Learning Across Compositions of Sequentail Tasks.
Incremental Development of Complex Behaviors.
Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus.
Variable Resolution Dynamic Programming.
Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture.
Self-improvement Based on Reinforcement Learning, Planning and Teaching.
Learning from Deliberated Reactivity.
Learning to Select a Model in a Changing World.
The Blind Leading the Blind: Mutual Refinement of Approximate Theories.
Learning Football Evaluation for a Walking Robot.
Learning to Avoid Obstacles Through Reinforcement.
Learning the Persistence of Actions in Reactive Control Rules.
On Becoming Decreasingly Reactive: Learning to Deliberate Minimally.
Decision-Theoretic Learning in an Action System.
Predicting Actions from Induction on Past Performance.
Machine Learning in the Combination of Expert Opinion Approach to IR.
A Goal-Based Approach to Intelligent Information Retrieval.
Query Learning Using an ANN with Adaptive Architecture.
Incremental Learning in a Probalistic Information Retrieval System.
Query Formulation Through Knowledge Acquisition.
Classification Trees for Information Retrieval.
A Probabilistic Retrieval Scheme for Cluster-based Adaptive Information Retrieval.
Learning in Intelligent Information Retrieval.
A Neural Network Approach to Constructive Induction.
Fringe-Like Feature Construction: A Comparative Study and a Unifying Scheme.
Feature Construction in Structural Decision Trees.
Constructive Induction in Knowledge-Based Neural Networks.
Learning Polynomial Functions by Feature Construction.
Relational Clichés: Constraining Induction During Relational Learning.
On the Effect of Instance Representation on Generalization.
Learning Concepts by Synthesizing Minimal Threshold Gate Networks.
Relations, Knowledge and Empirical Learning.
Constructive Induction of M-of-N Terms.
Constructive Induction in Theory Refinement.
The Need for Constructive Induction.
Comparison of Methods Based on Inverse Resolution.
Constructive Induction on Symbolic Features.
Discovering Production Rules with Higher Order Neural Networks.
Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning.
Opportunistic Constructive Induction.
Abstracting Concepts with Inverse Resolution.
A Hybrid Method for Feature Generation.
Informed Pruning in Constructive Induction.
Learning Variable Descriptors for Applying Heuristics Across CSP Problems.
A Transformational Approach to Constructive Induction.
Incremental Constructive Induction: An Instance-Based Approach.
Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control.
Simulating Stages of Human Cognitive Development With Connectionist Models.
Computer Modelling of Acquisition Orders in Child Language.
A Constraint-Motivated Model of Lexical Acquisition.
Variability Bias and Category Learning.
Adaptive Pattern-Oriented Chess.
The Acquisition of Human Planning Expertise.
Babel: A Psychologically Plausible Cross-Linguistic Model of Lexical and Syntactic Acquisition.
Internal World Models and Supervised Learning.
A Computational Model of Acquisition for Children's Addtion Strategies.
Modeling the Acquisition and Improvement of Motor Skkills.
Learning Words From Context.
The Importance of Causal Structure and Facts in Evaluating Explanations.
Combining Evidence of Deep and Surface Similarity.
A Prototype Based Symbolic Concept Learning System.
Generating Error Candidates for Assigning Blame in a Knowledge Base.
The Flexibility of Speculative Refinement.
Improving the Performance of Inconsistent Knowledge Bases via Combined Optimization Method.
Knowledge Refinement Using a High Level, Non-Technical Vocabulary.
A Domain-Independent Framework for Effective Experimentation in Planning.
Design Rationale Capture as Knowledge Acquisition.