Machine Learning: From Theory to Applications - Lecture Notes in Computer Science
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Published By Springer Berlin Heidelberg

9783540564836, 9783540475682

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Training a 3-node neural network is NP-complete

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_20 ◽

1993 ◽

pp. 9-28 ◽

Cited By ~ 17

Author(s):

Avrim L. Blum ◽

Ronald L. Rivest

Keyword(s):

Neural Network ◽

Np Complete

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Inference of finite automata using homing sequences

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_22 ◽

1993 ◽

pp. 51-73 ◽

Cited By ~ 21

Author(s):

Ronald L. Rivest ◽

Robert E. Schapire

Keyword(s):

Finite Automata

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Behavior-based learning to control IR oven heating: Preliminary investigations

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_33 ◽

1993 ◽

pp. 229-240

Author(s):

R. Chou ◽

P. Liu ◽

J. Vallino ◽

M. Y. Chiu

Keyword(s):

Behavior Based

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Cross references are features

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_26 ◽

1993 ◽

pp. 107-123 ◽

Cited By ~ 1

Author(s):

Robert W. Schwanke ◽

Michael A. Platoff

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Learning of rules for fault diagnosis in power supply networks

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_25 ◽

1993 ◽

pp. 93-105

Author(s):

R. Meunier ◽

R. Scheiterer ◽

A. Hecht

Keyword(s):

Fault Diagnosis ◽

Power Supply ◽

Supply Networks

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Adaptive search by learning from incomplete explanations of failures

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_24 ◽

1993 ◽

pp. 79-92

Author(s):

Neeraj Bhatnagar

Keyword(s):

Adaptive Search

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Strategic directions in machine learning

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_18 ◽

1993 ◽

pp. 1-4

Author(s):

Stephen José Hanson ◽

Werner Remmele ◽

Ronald L. Rivest

Keyword(s):

Machine Learning

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Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_34 ◽

1993 ◽

pp. 241-268 ◽

Cited By ~ 1

Author(s):

Thomas Petsche ◽

Bradley W. Dickinson

Keyword(s):

Neural Networks ◽

Fault Tolerant ◽

Receptive Fields ◽

Trellis Codes

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Introduction

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_29 ◽

1993 ◽

pp. 153-156 ◽

Cited By ~ 3

Author(s):

Stephen Josè Hanson

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L-ATMS: A tight integration of EBL and the ATMS

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-56483-7_28 ◽

1993 ◽

pp. 139-152

Author(s):

Kai Zercher

Keyword(s):

Tight Integration

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Machine Learning: From Theory to Applications - Lecture Notes in Computer ScienceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Training a 3-node neural network is NP-complete

Inference of finite automata using homing sequences

Behavior-based learning to control IR oven heating: Preliminary investigations

Cross references are features

Learning of rules for fault diagnosis in power supply networks

Adaptive search by learning from incomplete explanations of failures

Strategic directions in machine learning

Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks

Introduction

L-ATMS: A tight integration of EBL and the ATMS

Machine Learning: From Theory to Applications - Lecture Notes in Computer Science
Latest Publications