Blick ins Buch

Machine Learning: From Theory to Applications

Cooperative Research at Siemens and MIT

Stephen J. Hanson, Werner Remmele, Ronald L. Rivest (Herausgeber)

Buch | Softcover

VIII, 276 Seiten

1993 | 1993
Springer Berlin (Verlag)
978-3-540-56483-6 (ISBN)

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This volume includes some of the key research papers in thearea of machine learning produced at MIT and Siemens duringa three-year joint research effort. It includes papers onmany different styles of machine learning, organized intothree parts.Part I, theory, includes three papers on theoretical aspectsof machine learning. The first two use the theory ofcomputational complexity to derive some fundamental limitson what isefficiently learnable. The third provides anefficient algorithm for identifying finite automata.Part II, artificial intelligence and symbolic learningmethods, includes five papers giving an overview of thestate of the art and future developments in the field ofmachine learning, a subfield of artificial intelligencedealing with automated knowledge acquisition and knowledgerevision.Part III, neural and collective computation, includes fivepapers sampling the theoretical diversity and trends in thevigorous new research field of neural networks: massivelyparallel symbolic induction, task decomposition throughcompetition, phoneme discrimination, behavior-basedlearning, and self-repairing neural networks.

Ronald L. Rivest ist Professor für Elektrotechnik und Informatik am Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts, USA. Er arbeitet im Labor für Informatik und Künstliche Intelligenz (Computer Science and Artificial Intelligence Laboratory, CSAIL) und ist unter anderem Gründer der Gruppe Kryptographie und Informationssicherheit (Cryptography and Information Security Group). Seine Forschungsschwerpunkte liegen auf Kryptographie, Computer- und Netzwerksicherheit, elektronischer Abstimmung/Wahl und Algorithmen. Er ist ferner Mitbegründer der RSA Security Inc., einer weltweit agierenden Firma im Bereich Schutz von Online-Identitäten und digitalen Vermögenswerten sowie von Peppercoin, einem Anbieter von Finanzdienstleistungen.

Strategic directions in machine learning.- Training a 3-node neural network is NP-complete.- Cryptographic limitations on learning Boolean formulae and finite automata.- Inference of finite automata using homing sequences.- Adaptive search by learning from incomplete explanations of failures.- Learning of rules for fault diagnosis in power supply networks.- Cross references are features.- The schema mechanism.- L-ATMS: A tight integration of EBL and the ATMS.- Massively parallel symbolic induction of protein structure/function relationships.- Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks.- Phoneme discrimination using connectionist networks.- Behavior-based learning to control IR oven heating: Preliminary investigations.- Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks.

Erscheint lt. Verlag	30.3.1993
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	VIII, 276 p.
Verlagsort	Berlin
Sprache	englisch
Maße	155 x 233 mm
Gewicht	394 g
Themenwelt	Informatik ► Software Entwicklung ► User Interfaces (HCI)
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	algorithm • algorithms • Artificial Intelligence • Automat • Automata • Behavior-Based Learning • Case-Based Learning • Complexity • Fallbasiertes Lernen • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • Intelligence • Kybernetik • learning • Learning theory • Lernen • Lerntheorie • machine learning • Maschinelles Lernen • Maschinen-Lernen • Neural Computation • Neuronales Netz • Neuronales Rechnen • Verhaltensbasiertes Lernen
ISBN-10	3-540-56483-7 / 3540564837
ISBN-13	978-3-540-56483-6 / 9783540564836
Zustand	Neuware