Machine learning : proceedings of the seventh international conference (1990), University of Texas, Austin, Texas, June 21-23, 1990 /

Machine Learning Proceedings 1990.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores Corporativos: International Conference on Machine Learning University of Texas, Artificial Intelligence and Robotics Program (U.S.)
Otros Autores: Porter, Bruce, 1956-, Mooney, Raymond J. (Raymond Joseph)
Formato: Electrónico Congresos, conferencias eBook
Idioma:Inglés
Publicado: San Mateo, Calif. : Morgan Kaufmann Publishers, �1990.
Temas:
Machine learning > Congresses.
Apprentissage automatique > Congr�es.
COMPUTERS > General.
Machine learning
Machine-learning.
Machine learning > Congresses
Congress
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congr�es.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. Knowledge acquisition from examples usinmg maximal representation learning / S. Arun Kumar and S. Yegneshwar
  • KBG: a knowledge based generalizer / G. Bisson
  • Performance analysis of a probabalistic inductive learning system / K.C.C. Chan and A.K.C. Wong
  • A comparative study of ID3 and backpropagation for English text-to-speech mapping / T.G. Dietterich, H. Hild, and G. Bakiri
  • Learning from data with bounded inconsistency / H. Hirsh
  • Conceptual set covering: improving fit-and-split algorithms / C.M. Kadie
  • Incremental learning of rules and meta-rules / M. Schoenauer and M. Sebag
  • An incremental method for finding multivariate splits for decision trees / P.E. Utgoff and C.E. Brodley
  • Incremental induction of topologically minimal trees / W. Van de Velde.
  • 2. A rational analysis of categorization / J.R. Anderson and M. Matessa
  • Search control, utility, anf concept induction / B. Carlson, J. Weinberg, and D. Fisher
  • Graph clustering and model learning by data compression / J. Segen
  • 3. An analysis of representation shift in concept learning / W.E. Cohen
  • Learning procedures by environment-driven constructive induction / D.V. Hume
  • Beyond inversion of resolution / C. Rouveirol and J. Puget
  • 4. Genetic programming / H. de Garis
  • Improving the performance of genetic algorithms in automated discovery of parameters / N. Kadaba and K.E. Nygard
  • Using genetic alogorithms to learn disjunctive rules from examples / R. Andrew McCallum and K.A. Spackman
  • NEWBOOLE: a fast GBML system / P. Bonelli, A. Parodi, S. Sen and S. Wilson.
  • 5. Learning functions in k-DNF from reinforcement / L.P. Kaelbling
  • Is learning rate a good performance criterion for learning? / C. Sammut and J. Cribb
  • Active perception and reinforcement learning / S.D. Whitehead and D.H. Ballard
  • 6. Learning plans for competetive domains / S.L. Epstein
  • Explanations of empirically derived reactive plans / D.F. Gordon and J.J. Grefenstette
  • Learning and enforcement: stabilizing environments to facilitate activity / K.J. Hammond
  • Simulation-assisted learning by competioion: effects of noise differences between training model and target environment / C.L. Ramsey, A.C. Schultz, and J.J. Grefenstette
  • Integrated architecture for learning, planning, and reacting based on approximating dynamic programming / R.S. Sutton.
  • 7. Reducing real-world failures of approximate explanation-based rules / S.W. Bennett
  • Correcting and extending domain knowledge using outside guidance / J.E. Laird, M. Hucka, E.S. Yager, and C.M. Tuck
  • Acquisition of dynamic control knowledge for a robotic manipulator / A.W. Moore
  • Feature extraction and clustering of tactile impressions with connectionist models / M. Thint and P.P. Wang
  • 8. Generalizing the order of goals as an approach to generalizing number / H. Bostrom
  • Learning approximate control rules of high utility / W.W. Cohen
  • Applying abstraction and simplification to learn in intratible domains / N.S. Fann
  • Explanation-based learning with incomplete theories: a three-step approach / J. Genest, S. Matwin, and B. Plante
  • Using abductive recovery of failed proofs for problem solving by analogy / Y. Kodratoff.
  • Issues in the design of operator composition systems / S. Minton
  • Incremental learning of explanation patterns and their indices / A. Ram
  • 9. Integrated learning in a real domain / F. Bergadano, A. Giordana, L. Saitta, D. DeMarchi, and F. Brancadori
  • Incremental version-space merging / H. Hirsch
  • Average case analysis of conjunctive learning algorithms / M.J. Pazzani and W. Sarrett
  • ILS: a framework for multi-paradigmatic learning / B. Silver, W. Frawley, G. Iba, J. Vittal, and K. Bradford
  • An integrated framework of inducing rules from examples
  • Y. WU, S. Wanf, and Q. Zhou
  • 10. Adaptive parsing: a general method for learning idiosyncratic grammars / J.F. Lehman
  • A comparison of learning techniques in second language learning / S.L. Lytinen and C.E. Moon
  • Learning string patterns and tree patterns from examples
  • K. Ko, A. Marron, and W. Tzeng.
  • Learning with discrete multi-valued neurons / Z. Obradovic and I. Parberry
  • 11. The general utility problems in machine learning / L.B. Holder
  • A robust approach to numeric discovery / B. Nordhausen and P. Langley
  • More results on the complexity of knowlegge base refinement: belief networks / M. Valtorta.

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