Machine Learning Proceedings 1993
Proceedings of the Tenth International Conference on Machine Learning, University of Massachusetts, Amherst, June 27-29, 1993
- 1st Edition - July 1, 1993
- Imprint: Morgan Kaufmann
- Editor: Lawrence A. Birnbaum
- Language: English
- Paperback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 3 0 7 - 3
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 9 8 6 2 - 7
Machine Learning: Proceedings of the Tenth International Conference covers the papers presented at the Tenth International Conference on Machine Learning, held at Amherst,… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteMachine Learning: Proceedings of the Tenth International Conference covers the papers presented at the Tenth International Conference on Machine Learning, held at Amherst, Massachusetts in June 27-29, 1993. The book focuses on the advancements of techniques, practices, approaches, and methodologies in machine learning. The selection first offers information on automatic algorithm/model class selection, using decision trees to improve case-based learning, GALOIS, and multitask learning. Discussions focus on multitask connectionist learning in more detail; multitask decision trees; an algorithm for the incremental determination of the concept lattice; and empirical evaluation of GALOIS as a learning system. The text then examines the use of qualitative models to guide inductive learning; automation of path analysis for building causal models from data; and construction of hidden variables in Bayesian networks via conceptual clustering. The book ponders on synthesis of abstraction hierarchies for constraint satisfaction by clustering approximately equivalent objects; efficient domain-independent experimentation; learning search control knowledge for deep space network scheduling; and learning procedures from interactive natural language instructions. The selection is a dependable reference for researchers wanting to explore the field of machine learning.
PrefaceOrganizing Committee and Program CommitteeWorkshopsScheduleThe Evolution of Genetic Algorithms: Towards Massive ParallelismÉLÉNA: A Bottom-Up Learning MethodAddressing the Selective Superiority Problem: Automatic Algorithm/Model Class SelectionUsing Decision Trees to Improve Case-Based LearningGALOIS: An Order-Theoretic Approach to Conceptual ClusteringMultitask Learning: A Knowledge-Based Source of Inductive BiasUsing Qualitative Models to Guide Inductive LearningAutomating Path Analysis for Building Causal Models from DataConstructing Hidden Variables in Bayesian Networks Via Conceptual ClusteringLearning Symbolic Rules Using Artificial Neural NetworksSmall Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone NetworkConcept Sharing: A Means to Improve Multi-Concept LearningDiscovering DynamicsSynthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent ObjectsSKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky SurveysLearning From Entailment: An Application to Propositional Horn SentencesEfficient Domain-Independent ExperimentationLearning Search Control Knowledge for Deep Space Network SchedulingLearning Procedures from Interactive Natural Language InstructionsGeneralization Under Implication by Recursive Anti-UnificationSupervised Learning and Divide-and-Conquer: A Statistical ApproachHierarchical Learning in Stochastic Domains: Preliminary ResultsConstraining Learning with Search ControlScaling Up Reinforcement Learning for Robot ControlOvercoming Incomplete Perception with Utile Distinction MemoryExplanation Based Learning: A Comparison of Symbolic and Neural Network ApproachesCombinatorial Optimization in Inductive Concept LearningDecision Theoretic Subsampling for Induction on Large DatabasesLearning DNF Via Probabilistic Evidence CombinationExplaining and Generalizing Diagnostic DecisionsCombining Instance-Based and Model-Based LearningData Mining of Subjective Agricultural DataLookahead Feature Construction for Learning Hard ConceptsAdaptive NeuroControl: How Black Box and Simple can it beAn SE-Tree Based Characterization of the Induction ProblemDensity-Adaptive Learning and ForgettingEfficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal PruningCompiling Bayesian Networks into Neural NetworksA Reinforcement Learning Method for Maximizing Undiscounted RewardsATM Scheduling with Queuing Delay PredictionsOnline Learning with Random RepresentationsLearning from Queries and Examples with Tree-Structured BiasMulti-Agent Reinforcement Learning: Independent Vs. Cooperative AgentsBetter Learners Use Analogical Problem Solving SparinglyAuthor IndexSubject Index
- Edition: 1
- Published: July 1, 1993
- No. of pages (eBook): 540
- Imprint: Morgan Kaufmann
- Language: English
- Paperback ISBN: 9781558603073
- eBook ISBN: 9781483298627
Read Machine Learning Proceedings 1993 on ScienceDirect