Machine Learning: From Theory to Applications 1993

Stephen Jose Hanson, Werner Remmele, Ronald L. Rivest (Eds.): Machine Learning: From Theory to Applications - Cooperative Research at Siemens and MIT. Lecture Notes in Computer Science 661 Springer 1993, ISBN 3-540-56483-7

@proceedings{DBLP:conf/siemens/1993,
  editor    = {Stephen Jose Hanson and
               Werner Remmele and
               Ronald L. Rivest},
  title     = {Machine Learning: From Theory to Applications - Cooperative Research
               at Siemens and MIT},
  booktitle = {Machine Learning: From Theory to Applications},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {661},
  year      = {1993},
  isbn      = {3-540-56483-7},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Stephen Jose Hanson, Werner Remmele, Ronald L. Rivest: Strategic Directions in Machine Learning. 1-4

Part I: Theory

Ronald L. Rivest: Introduction. 7-7
Avrim Blum, Ronald L. Rivest: Training a 3-Node Neural Network is NP-Complete. 9-28
Michael J. Kearns, Leslie G. Valiant: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. 29-49
Ronald L. Rivest, Robert E. Schapire: Inference of Finite Automata Using Homing Sequences. 51-73

Part II: Artificial Intelligence and Symbolic Learning Methods

Werner Remmele: Introduction. 77-77
Neeraj Bhatnagar: Adaptive Search by Learning from Incomplete Explanations of Failures. 79-92
Regine Meunier, Ruxandra Scheiterer, Angelika Hecht: Learning of Rules for Fault Diagnosis in Power Supply Networks. 93-105
Robert W. Schwanke, Michael A. Platoff: Cross References are Features. 107-123
Gary L. Drescher: The Schema Mechanism. 125-138
Kai Zercher: L-ATMS: A Tight Integration of EBL and the ATMS. 139-152

Part III: Neural and Collective Computation

Stephen Jose Hanson: Introduction. 155-156
Richard H. Lathrop, Teresa A. Webster, Temple F. Smith, Patrick H. Winston: Massively Parallel Symbolic Induction of Protein Structure/Function Relationships. 157-173
Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto: Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. 175-202
Raymond L. Watrous: Phoneme Discrimination Using Connectionist Networks. 203-227
R. Chou, P. Liu, James R. Vallino, M. Y. Chiu: Behavior-Based Learning to Control IR Oven Heating: Preliminary Investigations. 229-240
Thomas Petsche, Bradley W. Dickinson: Trellis Codes, Receptive Fields, and Fault Tolerant, Self-Repairing Neural Networks. 241-268