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Proceedings of the Fifth International Conference on Machine Learning : June 12-15, 1988, University of Michigan, Ann Arbor, Michigan /
Machine Learning Proceedings 1988.
| Clasificación: | Libro Electrónico |
|---|---|
| Autores Corporativos: | , |
| Otros Autores: | |
| Formato: | Electrónico Congresos, conferencias eBook |
| Idioma: | Inglés |
| Publicado: |
San Mateo, Calif. :
Morgan Kaufmann, Publishers,
[1988]
|
| Colección: | Proceedings - international conference on machine learning ;
5th |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 111 | 2 | |a International Conference on Machine Learning |n (5th : |d 1988 : |c University of Michigan) | |
| 245 | 1 | 0 | |a Proceedings of the Fifth International Conference on Machine Learning : |b June 12-15, 1988, University of Michigan, Ann Arbor, Michigan / |c editor/program chair, John Laird. |
| 264 | 1 | |a San Mateo, Calif. : |b Morgan Kaufmann, Publishers, |c [1988] | |
| 264 | 4 | |c �1988 | |
| 300 | |a 1 online resource (vii, 467 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Proceedings - international conference on machine learning ; |v 5th | |
| 500 | |a "The American Association of Artificial Intelligence ... provided support for the organization and planning of the conference." | ||
| 500 | |a Includes index. | ||
| 505 | 0 | |a Empirical learning : Using a generalization hierarchy to learn from examples / Randy G. Kerber ; Tuning rule-based systems to their environments / Hans Tallis ; On asking the right questions / Brent J. Krawchuk and Ian H. Witten ; concept simplification and prediction accuracy / Douglas H. Fisher and Jeffrey C. Schlimmer ; Learning graph models of shape / Jakub Segen ; Learning categorical decision criteria in biomedical domains / Kent A. Spackman ; Conceptual clumping of binary vectors with Occam's Razor / Jakub Segen ; AutoClass: a Bayesian classification system / Peter Cheeseman, James Kelly, Matthew Self, John Stutz, Will Taylor, and Don Freeman ; Incremental multiple concept learning using experiments / Klaus P. Gross ; Trading off simplicity and coverage in incremental concept learning / Wayne Iba, James Wogulis and Pat Langley ; Deferred commitment in UNIMEM: waiting to learn / Michael Lebowitz ; Experiments on the costs and benefits of windowing in ID3 / Jarryl Wirth and Jason Catlett ; Improved decision trees: a generalized version of ID3 / Jie Cheng, Usama M. Fayyad, Keki B. Irani and Zhaogang Qian ; ID5: in incremental ID3 / Paul E. Utgoff ; Using weighted networks to represent classification knowledge in noisy domains / Ming Tan and Larry Eshelman. | |
| 505 | 0 | |a Genetic learning : An empirical comparison of genetic and decision-tree classifiers / J.R. Quinlan ; Population size in classifier systems / George G. Robertson ; Representation and hidden bias: gray vs. binary coding for genetic algorithms / Richard A. Caruana and J. David Schaffer ; Classifier systems with hamming weights / Lawrence Davis and David K. Young ; Midgard: a genetic approach to adaptive load balancing for distributed systems / Adrian V. Sannier II and Erik D. Goodman -- Connectionist learning : Some interesting properties of a connectionist inductive learning system / Edward J. Wisniewski and James A. Anderson ; Competitive reinforcement learning / Kenton J. Lynne ; Connectionist learning of expert backgammon evaluations / G. Tesauro ; Building and using mental models in a sensory-motor domain: a connectionist approach / Bartlett W. Mel. | |
| 505 | 0 | |a Explanation-based learning : Reasoning about operationality for explanation-based learning / Haym Hirsh ; Boundaries of operationality / Michael S. Braverman and Stuart J. Russell ; On the tractability of learning from incomplete theories / Sridhar Mahadevan and Prasad Tadepalli ; Active explanation reduction: an approach to the multiple explanations problem / Shankar A. Rajamoney and Gerald F. DeJong ; Generalizing number and learning from multiple examples in explanation based learning / William W. Cohen ; Generalizing the order of operators in macro-operators / Raymond J. Mooney ; Using Experienced-based learning in game playing / Kenneth A. De Jong and Alan C. Schultz -- Integrated explanation-based and empirical learning : Integrated learning with incorrect and incomplete theories / Michael J. Pazzani ; An approach based on integrated learning to generating stories from stories / Claudio Carpiento ; A knowledge intensive approach to concept induction -- Case-based learning : Learning to program by examining and modifying cases / Robert S. Williams. | |
| 505 | 0 | |a Machine discovery : Theory discovery and the hypothesis language / Kevin T. Kelly ; Machine invention of first order predicates by inverting resolution / Stephen Muggleton and Wray Buntine ; The interdependencies of theory formation, revision, and experimentation / Brian Falkenhainer and Shankar Rajamoney ; A hill-climbing approach to machine discovery / Donald Rose and Pat Langley ; Reduction: a practical mechanism of searching for regularity in data / Yi-Hua Wu -- Formal models of concept learning : Extending the valiant learning model / Jonathan Amsterdam ; Learning systems of first-order rules / Nicolas Helft ; Two new frameworks for learning / B.K. Natarajan and P. Tadepalli ; Hypothesis filtering: a practical approach to reliable learning / Oren Etzioni -- Experimental results in machine learning : Diffy-S: learning robot operator schemata from examples / Carl M. Kadie ; Experimental results from an evaluation of algorithms that learn to control dynamic systems / Claude Sammut ; Utilizing experience for improving the tactical manager / Michael D. Erickson and Jan M. Zytkow -- Computational impact of learning and forgetting : Some chunks are expensive / Milind Tambe and Allen Newell -- The role of forgetting in learning / Shaul Markovitch and Paul D. Scott. | |
| 588 | 0 | |a Print version record. | |
| 504 | |a ReferencesChapter 7. Conceptual Clumping of Binary Vectors with Occam's Razor; Abstract; 1. Introduction; 2. Cluster configuration cost; 3. Finding clusters by minimizing the configuration cost; 4. Concluding remarks; References; Chapter 8. AutoClass: A Bayesian Classification System; Abstract; 1 Introduction; 2 Overview of Bayesian Classification; 3 The AutoClass II Program; 4 Extensions to the Model; 5 Results; 6 Conclusion; References; Chapter 9. Incremental Multiple Concept Learning Using Experiments; Abstract; 1. Introduction; 2. Terminology; 3. The primitive operations. | ||
| 520 | |a Machine Learning Proceedings 1988. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Machine learning |v Congresses. | |
| 650 | 6 | |a Apprentissage automatique |0 (CaQQLa)201-0131435 |v Congr�es. |0 (CaQQLa)201-0378219 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 655 | 2 | |a Congress |0 (DNLM)D016423 | |
| 655 | 7 | |a proceedings (reports) |2 aat |0 (CStmoGRI)aatgf300027316 | |
| 655 | 7 | |a Conference papers and proceedings |2 fast |0 (OCoLC)fst01423772 | |
| 655 | 7 | |a Conference papers and proceedings. |2 lcgft | |
| 655 | 7 | |a Actes de congr�es. |2 rvmgf |0 (CaQQLa)RVMGF-000001049 | |
| 700 | 1 | |a Laird, John, |d 1954- |e editor. | |
| 710 | 2 | |a American Association for Artificial Intelligence. | |
| 776 | 0 | 8 | |i Print version: |a International Conference on Machine Learning (5th : 1988 : University of Michigan). |t Proceedings of the Fifth International Conference on Machine Learning |z 0934613648 |w (DLC) 88012799 |w (OCoLC)18017017 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780934613644 |z Texto completo |