000			03012nam a22003617a 4500
003			OSt
005			20250707155453.0
008			250707b |||||||| |||| 00| 0 eng d
020			_a9783642010811 _q(hardcover)
040			_beng _cDLC _dDLC _erda _aSai University Library
082			_222 _a006.31 _bHAS
245	0	0	_aFoundations of computational intelligence _cAboul-Ella Hassanien, Ajith Abraham, Athanasios V. Vasilakos, Witold Pedrycz editors _nVolume 1, _pLearning and approximation /
260			_aBerlin , Heidelberg : _bSpringer, _c2009
264		1	_aBerlin , Heidelberg : _bSpringer, _c2009
264		4	_c©2009
300			_a xii, 397 pages : _billustrations ; _c25 cm
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
490	1		_aStudies in computational intelligence ; _vv. 201
500			_aGifted by Professor Ajith Abraham
504			_aIncludes Index and bibliographic references
520			_aLearning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, geometric problems, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, randomization techniques, real-world applications, scheduling problems and so on. The past years have witnessed a large number of interesting applications using various techniques of Computational Intelligence such as rough sets, connectionist learning; fuzzy logic; evolutionary computing; artificial immune systems; swarm intelligence; reinforcement learning, intelligent multimedia processing etc.. In spite of numerous successful applications of Computational Intelligence in business and industry, it is sometimes difficult to explain the performance of these techniques and algorithms from a theoretical perspective. Therefore, we encouraged authors to present original ideas dealing with the incorporation of different mechanisms of Computational Intelligent dealing with Learning and Approximation algorithms and underlying processes. This edited volume comprises 15 chapters, including an overview chapter, which provides an up-to-date and state-of-the art research on the application of Computational Intelligence for learning and approximation
650		0	_aComputational learning theory
650		0	_aComputational intelligence
650		0	_aArtificial intelligence
700	1		_aHassanien, Aboul Ella _eeditor
700	1		_aAbraham Ajith _eeditor
700	1		_aAthanasios V. Vasilakos _eeditor
700	1		_aPedrycz, Witold _eeditor
830			_aStudies in computational intelligence ; _vv. 201
942			_2ddc _cBK
999			_c6836 _d6836