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020 _a0262133288
040 _aGR-AtMCL
_dGR-AtMCL
_beng
_eAACR2
100 _aMehrotra, Kishan
_99944
245 1 0 _aElements of artificial neural networks /
_cKishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka.
246 3 0 _aArtificial neural networks
260 _aCambridge, Mass. :
_bMIT Press,
_cc1997.
300 _axiv, 344 p. :
_bill. ;
_c24 cm.
440 0 _aComplex adaptive systems
504 _aIncludes bibliographical references (p. [331]-338) and index.
520 _aElements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods.
650 0 _aNeural networks (Computer science)
_99947
650 0 _aComputing
_99851
700 _aMohan, Chilukuri K.
_99945
700 _aRanka, Sanjay
_99946
942 _cBK
999 _c9868
_d9868