Knowledge-based neurocomputing /:
Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©2000.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system. Contributors : C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada. |
| Beschreibung: | 1 online resource (xiv, 486 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 0585355010 9780585355016 9780262528733 0262528738 9780262270496 0262270498 0262032740 9780262032742 |
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| 505 | 0 | 0 | |t Knowledge-based neurocomputing : past, present, and future -- |t Architectures and techniques for knowledge-based neurocomputing -- |t Symbolic knowledge representation in recurrent neural networks : insights from theoretical models of computation -- |t Tutorial on neurocomputing of structures -- |t Structural learning and rule discovery -- |t VL₁ANN : transformation of rules to artificial neural networks -- |t Integration of heterogeneous sources of partial domain knowledge -- |t Approximation of differential equations using neural networks -- |t Fynesse : a hybrid architecture for self-learning control -- |t Data mining techniques for designing neural network time series predictors -- |t Extraction of decision trees from artificial networks -- |t Extraction of linguistic rules from data via neural networks and fuzzy approximation -- |t Neural knowledge processing in expert systems. |
| 520 | 8 | |a Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system. Contributors : C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada. | |
| 546 | |a English. | ||
| 650 | 0 | |a Neural computers. |0 http://id.loc.gov/authorities/subjects/sh87008041 | |
| 650 | 0 | |a Expert systems (Computer science) |0 http://id.loc.gov/authorities/subjects/sh85046450 | |
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| 700 | 1 | |a Cloete, Ian, |e editor. | |
| 700 | 1 | |a Zurada, Jacek M., |e editor. | |
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| contents | Knowledge-based neurocomputing : past, present, and future -- Architectures and techniques for knowledge-based neurocomputing -- Symbolic knowledge representation in recurrent neural networks : insights from theoretical models of computation -- Tutorial on neurocomputing of structures -- Structural learning and rule discovery -- VL₁ANN : transformation of rules to artificial neural networks -- Integration of heterogeneous sources of partial domain knowledge -- Approximation of differential equations using neural networks -- Fynesse : a hybrid architecture for self-learning control -- Data mining techniques for designing neural network time series predictors -- Extraction of decision trees from artificial networks -- Extraction of linguistic rules from data via neural networks and fuzzy approximation -- Neural knowledge processing in expert systems. |
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| dewey-search | 006.3/2 |
| dewey-sort | 16.3 12 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system. Contributors : C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. 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| id | ZDB-4-EBA-ocm47011985 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:15:14Z |
| institution | BVB |
| isbn | 0585355010 9780585355016 9780262528733 0262528738 9780262270496 0262270498 0262032740 9780262032742 |
| language | English |
| oclc_num | 47011985 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xiv, 486 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2000 |
| publishDateSearch | 2000 |
| publishDateSort | 2000 |
| publisher | MIT Press, |
| record_format | marc |
| spelling | Knowledge-based neurocomputing / edited by Ian Cloete and Jacek M. Zurada. Cambridge, Mass. : MIT Press, ©2000. 1 online resource (xiv, 486 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rdaft Includes bibliographical references and index. Print version record. Knowledge-based neurocomputing : past, present, and future -- Architectures and techniques for knowledge-based neurocomputing -- Symbolic knowledge representation in recurrent neural networks : insights from theoretical models of computation -- Tutorial on neurocomputing of structures -- Structural learning and rule discovery -- VL₁ANN : transformation of rules to artificial neural networks -- Integration of heterogeneous sources of partial domain knowledge -- Approximation of differential equations using neural networks -- Fynesse : a hybrid architecture for self-learning control -- Data mining techniques for designing neural network time series predictors -- Extraction of decision trees from artificial networks -- Extraction of linguistic rules from data via neural networks and fuzzy approximation -- Neural knowledge processing in expert systems. Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system. Contributors : C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada. English. Neural computers. http://id.loc.gov/authorities/subjects/sh87008041 Expert systems (Computer science) http://id.loc.gov/authorities/subjects/sh85046450 Ordinateurs neuronaux. Systèmes experts (Informatique) COMPUTERS Neural Networks. bisacsh Expert systems (Computer science) fast Neural computers fast COMPUTER SCIENCE/General Cloete, Ian, editor. Zurada, Jacek M., editor. has work: Knowledge-based neurocomputing (Text) https://id.oclc.org/worldcat/entity/E39PCGFB9wT3jVC9vJtrCHWRrq https://id.oclc.org/worldcat/ontology/hasWork Print version: Knowledge-based neurocomputing. Cambridge, Mass. : MIT Press, ©2000 0262032740 (DLC) 99041770 (OCoLC)42002763 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=49735 Volltext |
| spellingShingle | Knowledge-based neurocomputing / Knowledge-based neurocomputing : past, present, and future -- Architectures and techniques for knowledge-based neurocomputing -- Symbolic knowledge representation in recurrent neural networks : insights from theoretical models of computation -- Tutorial on neurocomputing of structures -- Structural learning and rule discovery -- VL₁ANN : transformation of rules to artificial neural networks -- Integration of heterogeneous sources of partial domain knowledge -- Approximation of differential equations using neural networks -- Fynesse : a hybrid architecture for self-learning control -- Data mining techniques for designing neural network time series predictors -- Extraction of decision trees from artificial networks -- Extraction of linguistic rules from data via neural networks and fuzzy approximation -- Neural knowledge processing in expert systems. Neural computers. http://id.loc.gov/authorities/subjects/sh87008041 Expert systems (Computer science) http://id.loc.gov/authorities/subjects/sh85046450 Ordinateurs neuronaux. Systèmes experts (Informatique) COMPUTERS Neural Networks. bisacsh Expert systems (Computer science) fast Neural computers fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh87008041 http://id.loc.gov/authorities/subjects/sh85046450 |
| title | Knowledge-based neurocomputing / |
| title_alt | Knowledge-based neurocomputing : past, present, and future -- Architectures and techniques for knowledge-based neurocomputing -- Symbolic knowledge representation in recurrent neural networks : insights from theoretical models of computation -- Tutorial on neurocomputing of structures -- Structural learning and rule discovery -- VL₁ANN : transformation of rules to artificial neural networks -- Integration of heterogeneous sources of partial domain knowledge -- Approximation of differential equations using neural networks -- Fynesse : a hybrid architecture for self-learning control -- Data mining techniques for designing neural network time series predictors -- Extraction of decision trees from artificial networks -- Extraction of linguistic rules from data via neural networks and fuzzy approximation -- Neural knowledge processing in expert systems. |
| title_auth | Knowledge-based neurocomputing / |
| title_exact_search | Knowledge-based neurocomputing / |
| title_full | Knowledge-based neurocomputing / edited by Ian Cloete and Jacek M. Zurada. |
| title_fullStr | Knowledge-based neurocomputing / edited by Ian Cloete and Jacek M. Zurada. |
| title_full_unstemmed | Knowledge-based neurocomputing / edited by Ian Cloete and Jacek M. Zurada. |
| title_short | Knowledge-based neurocomputing / |
| title_sort | knowledge based neurocomputing |
| topic | Neural computers. http://id.loc.gov/authorities/subjects/sh87008041 Expert systems (Computer science) http://id.loc.gov/authorities/subjects/sh85046450 Ordinateurs neuronaux. Systèmes experts (Informatique) COMPUTERS Neural Networks. bisacsh Expert systems (Computer science) fast Neural computers fast |
| topic_facet | Neural computers. Expert systems (Computer science) Ordinateurs neuronaux. Systèmes experts (Informatique) COMPUTERS Neural Networks. Neural computers |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=49735 |
| work_keys_str_mv | AT cloeteian knowledgebasedneurocomputing AT zuradajacekm knowledgebasedneurocomputing |