The Resource Machine learning : proceedings of the tenth international conference, University of Massachusetts, Amherst, June 27-29, 1993, Paul Utgoff, ML93 chair

Machine learning : proceedings of the tenth international conference, University of Massachusetts, Amherst, June 27-29, 1993, Paul Utgoff, ML93 chair

Label
Machine learning : proceedings of the tenth international conference, University of Massachusetts, Amherst, June 27-29, 1993
Title
Machine learning
Title remainder
proceedings of the tenth international conference, University of Massachusetts, Amherst, June 27-29, 1993
Statement of responsibility
Paul Utgoff, ML93 chair
Creator
Contributor
Subject
Genre
Language
eng
Cataloging source
NRC
Illustrations
illustrations
Index
index present
LC call number
Q325.5
LC item number
.I57 1993
Literary form
non fiction
http://bibfra.me/vocab/lite/meetingDate
1993
http://bibfra.me/vocab/lite/meetingName
International Conference on Machine Learning
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorDate
1951-
http://library.link/vocab/relatedWorkOrContributorName
Utgoff, Paul E.
http://library.link/vocab/subjectName
  • Machine learning
  • induction
  • réseau neuronal
  • multitâche
  • apprentissage base-cas
  • apprentissage
  • algorithme génétique
  • apprentissage machine
  • Machine learning
  • Machine-learning
  • Apprentissage automatique
Label
Machine learning : proceedings of the tenth international conference, University of Massachusetts, Amherst, June 27-29, 1993, Paul Utgoff, ML93 chair
Instantiates
Publication
Note
"Tenth International Conference on Machine Learning, held at Amherst, Massachusetts, during June 27-29, 1993"--Pref
Bibliography note
Includes bibliographical references and indexes
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • The Evolution of Genetic Algorithms: Towards Massive Parallelism / Shumeet Baluja -- ELENA: A Bottom-Up Learning Method / Pierre Brezellec and Henry Soldano -- Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection / Carla E. Brodley -- Using Decision Trees to Improve Case-Based Learning / Claire Cardie -- GALOIS: An order-theoretic approach to conceptual clustering / Claudio Carpineto and Giovanni Romano -- Multitask Learning: A Knowledge-Based Source of Inductive Bias / Richard A. Caruana -- Using Qualitative Models to Guide Inductive Learning / Peter Clark and Stan Matwin -- Automating Path Analysis for Building Causal Models from Data / Paul R. Cohen, Adam Carlson, Lisa Ballesteros and Robert St. Amant -- Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering / Dennis Connolly -- Learning Symbolic Rules Using Artificial Neural Networks / Mark W. Craven and Jude W. Shavlik
  • Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network / Andrea Pohoreckyj Danyluk and Foster John Provost -- Concept Sharing: A Means to Improve Multi-Concept Learning / Piew Datta and Dennis Kibler -- Discovering Dynamics / Saso Dzeroski and Ljupco Todorovski -- Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects / Thomas Ellman -- SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys / Usama M. Fayyad, Nicholas Weir and S. Djorgovski -- Learning From Entailment: An Application to Propositional Horn Sentences / Michael Frazier and Leonard Pitt -- Efficient Domain-Independent Experimentation / Yolanda Gil -- Learning Search Control Knowledge for Deep Space Network Scheduling / Jonathan Gratch, Steve Chien and Gerald DeJong -- Learning procedures from interactive natural language instructions / Scott B. Huffman and John E. Laird
  • Generalization under Implication by Recursive Anti-unification / Peter Idestam-Almquist -- Supervised learning and divide-and-conquer: A statistical approach / Michael I. Jordan and Robert A. Jacobs -- Hierarchical Learning in Stochastic Domains: Preliminary Results / Leslie Pack Kaelbling -- Constraining Learning with Search Control / Jihie Kim and Paul S. Rosenbloom -- Sealing Up Reinforcement Learning for Robot Control / Long-Ji Lin -- Overcoming Incomplete Perception with Utile Distinction Memory / R. Andrew McCallum -- Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches / Tom M. Mitchell and Sebastian B. Thrun -- Combinatorial optimization in inductive concept learning / Dunja Mladenic -- Decision Theoretic Subsampling for Induction on Large Databases / Ron Musick, Jason Catlett and Stuart Russell -- Learning DNF Via Probabilistic Evidence Combination / Steven W. Norton and Haym Hirsh
  • Explaining and Generalizing Diagnostic Decisions / Paul O'Rorke, Yousri El Fattah and Margaret Elliott -- Combining Instance-Based and Model-Based Learning / J.R. Quinlan -- Data Mining of Subjective Agricultural Data / R. Bharat Rao, Thomas B. Voigt and Thomas W. Fermanian -- Lookahead Feature Construction for Learning Hard Concepts / Harish Ragavan and Larry Rendell -- Adaptive NeuroControl: How Black Box and Simple can it be / Jean Michel Renders, Hugues Bersini and Marco Saerens -- An SE-tree based Characterization of the Induction Problem / Ron Rymon -- Density-Adaptive Learning and Forgetting / Marcos Salganicoff -- Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning / Jeffrey C. Schlimmer -- Compiling Bayesian Networks into Neural Networks / Eddie Schwalb -- A Reinforcement Learning Method for Maximizing Undiscounted Rewards / Anton Schwartz -- ATM Scheduling with Queuing Delay Predictions / Daniel B. Schwartz
  • Online Learning with Random Representations / Richard S. Sutton and Steven D. Whitehead -- Learning from Queries and Examples with Tree-structured Bias / Prasad Tadepalli -- Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents / Ming Tan -- Better Learners Use Analogical Problem Solving Sparingly / Kurt Van Lehn and Randolph M. Jones
Dimensions
28 cm
Extent
v, [7], 348 pages
Isbn
9781558603073
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
  • (OCoLC)29321956
  • (OCoLC)ocm29321956
Label
Machine learning : proceedings of the tenth international conference, University of Massachusetts, Amherst, June 27-29, 1993, Paul Utgoff, ML93 chair
Publication
Note
"Tenth International Conference on Machine Learning, held at Amherst, Massachusetts, during June 27-29, 1993"--Pref
Bibliography note
Includes bibliographical references and indexes
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • The Evolution of Genetic Algorithms: Towards Massive Parallelism / Shumeet Baluja -- ELENA: A Bottom-Up Learning Method / Pierre Brezellec and Henry Soldano -- Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection / Carla E. Brodley -- Using Decision Trees to Improve Case-Based Learning / Claire Cardie -- GALOIS: An order-theoretic approach to conceptual clustering / Claudio Carpineto and Giovanni Romano -- Multitask Learning: A Knowledge-Based Source of Inductive Bias / Richard A. Caruana -- Using Qualitative Models to Guide Inductive Learning / Peter Clark and Stan Matwin -- Automating Path Analysis for Building Causal Models from Data / Paul R. Cohen, Adam Carlson, Lisa Ballesteros and Robert St. Amant -- Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering / Dennis Connolly -- Learning Symbolic Rules Using Artificial Neural Networks / Mark W. Craven and Jude W. Shavlik
  • Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network / Andrea Pohoreckyj Danyluk and Foster John Provost -- Concept Sharing: A Means to Improve Multi-Concept Learning / Piew Datta and Dennis Kibler -- Discovering Dynamics / Saso Dzeroski and Ljupco Todorovski -- Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects / Thomas Ellman -- SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys / Usama M. Fayyad, Nicholas Weir and S. Djorgovski -- Learning From Entailment: An Application to Propositional Horn Sentences / Michael Frazier and Leonard Pitt -- Efficient Domain-Independent Experimentation / Yolanda Gil -- Learning Search Control Knowledge for Deep Space Network Scheduling / Jonathan Gratch, Steve Chien and Gerald DeJong -- Learning procedures from interactive natural language instructions / Scott B. Huffman and John E. Laird
  • Generalization under Implication by Recursive Anti-unification / Peter Idestam-Almquist -- Supervised learning and divide-and-conquer: A statistical approach / Michael I. Jordan and Robert A. Jacobs -- Hierarchical Learning in Stochastic Domains: Preliminary Results / Leslie Pack Kaelbling -- Constraining Learning with Search Control / Jihie Kim and Paul S. Rosenbloom -- Sealing Up Reinforcement Learning for Robot Control / Long-Ji Lin -- Overcoming Incomplete Perception with Utile Distinction Memory / R. Andrew McCallum -- Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches / Tom M. Mitchell and Sebastian B. Thrun -- Combinatorial optimization in inductive concept learning / Dunja Mladenic -- Decision Theoretic Subsampling for Induction on Large Databases / Ron Musick, Jason Catlett and Stuart Russell -- Learning DNF Via Probabilistic Evidence Combination / Steven W. Norton and Haym Hirsh
  • Explaining and Generalizing Diagnostic Decisions / Paul O'Rorke, Yousri El Fattah and Margaret Elliott -- Combining Instance-Based and Model-Based Learning / J.R. Quinlan -- Data Mining of Subjective Agricultural Data / R. Bharat Rao, Thomas B. Voigt and Thomas W. Fermanian -- Lookahead Feature Construction for Learning Hard Concepts / Harish Ragavan and Larry Rendell -- Adaptive NeuroControl: How Black Box and Simple can it be / Jean Michel Renders, Hugues Bersini and Marco Saerens -- An SE-tree based Characterization of the Induction Problem / Ron Rymon -- Density-Adaptive Learning and Forgetting / Marcos Salganicoff -- Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning / Jeffrey C. Schlimmer -- Compiling Bayesian Networks into Neural Networks / Eddie Schwalb -- A Reinforcement Learning Method for Maximizing Undiscounted Rewards / Anton Schwartz -- ATM Scheduling with Queuing Delay Predictions / Daniel B. Schwartz
  • Online Learning with Random Representations / Richard S. Sutton and Steven D. Whitehead -- Learning from Queries and Examples with Tree-structured Bias / Prasad Tadepalli -- Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents / Ming Tan -- Better Learners Use Analogical Problem Solving Sparingly / Kurt Van Lehn and Randolph M. Jones
Dimensions
28 cm
Extent
v, [7], 348 pages
Isbn
9781558603073
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
  • (OCoLC)29321956
  • (OCoLC)ocm29321956

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