PERSPECTIVES OF NEURAL-SYMBOLIC INTEGRATION (HB)

HAMMER B.

ISBN 10: 354073953X / ISBN 13: 9783540739531

Verlag: SP SPRINGER, 2007

Beschreibung

Brand New! . "This is an International Edition." Book is In New condition and ship within One Working Day Tracking Number Provided by Customer 12-24 In To Hour, Deliver by FedEx & Aramex, UPS, & USPS Act. Order can be delivered worldwide With In 7-10 Working day Delivery. Ship from India & United States.

Bestandsnummer des Verkäufers CBSBOOKS26479

Bibliografische Details

• Titel: PERSPECTIVES OF NEURAL-SYMBOLIC INTEGRATION (HB)
• Verlag: SP SPRINGER
• Erscheinungsjahr: 2007
• Sprache: Englisch
• ISBN-10: 354073953X
• ISBN-13: 9783540739531
• Einband: Hardcover
• Auflage: Internationale Ausgabe
• Zustand: Brand New

Über diesen Titel

Inhaltsangabe

When it comes to robotics and bioinformatics, the Holy Grail everyone is seeking is how to dovetail logic-based inference and statistical machine learning. This volume offers some possible solutions to this eternal problem. Edited with flair and sensitivity by Hammer and Hitzler, the book contains state-of-the-art contributions in neural-symbolic integration, covering `loose' coupling by means of structure kernels or recursive models as well as `strong' coupling of logic and neural networks.

Von der hinteren Coverseite

The human brain possesses the remarkable capability of understanding, interpreting, and producing language, structures, and logic. Unlike their biological counterparts, artificial neural networks do not form such a close liason with symbolic reasoning: logic-based inference mechanisms and statistical machine learning constitute two major and very different paradigms in artificial intelligence with complementary strengths and weaknesses. Modern application scenarios in robotics, bioinformatics, language processing, etc., however require both the efficiency and noise-tolerance of statistical models and the generalization ability and high-level modelling of structural inference meachanisms. A variety of approaches has therefore been proposed for combining the two paradigms.

This carefully edited volume contains state-of-the-art contributions in neural-symbolic integration, covering `loose' coupling by means of structure kernels or recursive models as well as `strong' coupling of logic and neural networks. It brings together a representative selection of results presented by some of the top researchers in the field, covering theoretical foundations, algorithmic design, and state-of-the-art applications in robotics and bioinformatics.