| From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data 2002 Edition Contributor(s): Apolloni, Bruno (Editor), Kurfess, Franz (Editor) |
|
 |
ISBN: 0306474026 ISBN-13: 9780306474026 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: October 2002 Annotation: 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 Intelligence 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 then 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. |
| Additional Information |
| BISAC Categories: - Computers | Neural Networks |
| Dewey: 006.32 |
| LCCN: 2002034144 |
| Physical Information: 0.94" H x 7" W x 10" (2.06 lbs) 388 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: One high-level ability of the human brain is to understand what it has learned. This seems to be the crucial advantage in comparison to the brain activity of other primates. At present we are technologically almost ready to artificially reproduce human brain tissue, but we still do not fully understand the information processing and the related biological mechanisms underlying this ability. Thus an electronic clone of the human brain is still far from being realizable. At the same time, around twenty years after the revival of the connectionist paradigm, we are not yet satisfied with the typical subsymbolic attitude of devices like neural networks: we can make them learn to solve even difficult problems, but without a clear explanation of why a solution works. Indeed, to widely use these devices in a reliable and non elementary way we need formal and understandable expressions of the learnt functions. of being tested, manipulated and composed with These must be susceptible other similar expressions to build more structured functions as a solution of complex problems via the usual deductive methods of the Artificial Intelligence. Many effort have been steered in this directions in the last years, constructing artificial hybrid systems where a cooperation between the sub symbolic processing of the neural networks merges in various modes with symbolic algorithms. In parallel, neurobiology research keeps on supplying more and more detailed explanations of the low-level phenomena responsible for mental processes. |