001387539 000__ 03264cam\a2200469Mi\4500 001387539 001__ 1387539 001387539 003__ MaCbMITP 001387539 005__ 20240325105118.0 001387539 006__ m\\\\\o\\d\\\\\\\\ 001387539 007__ cr\cn\nnnunnun 001387539 008__ 990727s1999\\\\maua\\\\ob\\\\001\0\eng\d 001387539 020__ $$a9780262270496 001387539 020__ $$a0262270498 001387539 020__ $$a9780262032742$$q(hardcover) 001387539 020__ $$a0262032740$$q(hardcover) 001387539 035__ $$a(OCoLC)1014476946$$z(OCoLC)1014395921$$z(OCoLC)1014423830$$z(OCoLC)1014488864 001387539 035__ $$a(OCoLC-P)1014476946 001387539 040__ $$aOCoLC-P$$beng$$erda$$epn$$cOCoLC-P 001387539 050_4 $$aQA76.87 001387539 08204 $$a006.3/2$$221 001387539 24500 $$aKnowledge-based neurocomputing /$$cedited by Ian Cloete and J.M. Zurada. 001387539 264_1 $$aCambridge, Massachusetts :$$bThe MIT Press,$$c[1999] 001387539 264_4 $$c©1999 001387539 300__ $$a1 online resource (xiv, 486 pages) :$$billustrations 001387539 336__ $$atext$$btxt$$2rdacontent 001387539 337__ $$acomputer$$bc$$2rdamedia 001387539 338__ $$aonline resource$$bcr$$2rdacarrier 001387539 506__ $$aAccess limited to authorized users. 001387539 520__ $$aLooking at ways to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system.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.ContributorsC. 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 001387539 588__ $$aOCLC-licensed vendor bibliographic record. 001387539 650_0 $$aExpert systems (Computer science) 001387539 650_0 $$aNeural computers. 001387539 653__ $$aCOMPUTER SCIENCE/General 001387539 655_0 $$aElectronic books 001387539 7001_ $$aCloete, Ian,$$eeditor. 001387539 7001_ $$aZurada, Jacek M.,$$eeditor. 001387539 852__ $$bebk 001387539 85640 $$3MIT Press$$uhttps://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/4070.001.0001?locatt=mode:legacy$$zOnline Access through The MIT Press Direct 001387539 85642 $$3OCLC metadata license agreement$$uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 001387539 909CO $$ooai:library.usi.edu:1387539$$pGLOBAL_SET 001387539 980__ $$aBIB 001387539 980__ $$aEBOOK 001387539 982__ $$aEbook 001387539 983__ $$aOnline