From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data Издательство: Springer, 2002 г Твердый переплет, 410 стр ISBN 0306474026 инфо 3750n.

Proceedings of the International School on Neural Nets "ER Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data, held 25 February - 7 March, 2002,аэфэн in Erice, Sicily, Italy The book aims to propose a theoretical and applicatory framework for extracting formal rules from data To this end recent approaches in relevant disciplines are examined that bring together two typical goals of conventional Artificial Intelliблфзфgence and connectionism -- respectively,deducing within an axiomatic shell formal rules about a phenomenon and inferring the actual behavior of it from examples -- into a challenging inferential framework where we learn from data and understand what we have learned The goal is to obtain a translation of the subsymbolic structure of the data -- stored in the synapses of a neural network -- into formal properties described by rules To capture this journey from synapses to rules and thбсййрen render it manageable for real world learning tasks, the contributions deal in depth with the following aspects: i theoretical foundations of learning algorithms and soft computing; ii intimate relationships between symbolic and subsymbolic reasoning methods; iii integration of the related hosting architectures in both physiological and artificial brain Авторы Bruno Apolloni Franz Kurfess.

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