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710	2		_aSpringerLink (Online service) _9106996
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008			180522s2019 gw a s |||| 0|eng d
020			_a9783319918396
024	7		_a10.1007/978-3-319-91839-6 _2doi
040			_bspa _aES-MaUEC _cES-MaUEC
050		4	_aQA402.5 _b2019 EB
100	1		_aAbouEisha, Hassan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671184
245	1	0	_aExtensions of Dynamic Programming for Combinatorial Optimization and Data Mining _cby Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, Mikhail Moshkov.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019.
300			_a1 recurso en línea (XVI, 280 páginas) _b72 ilustraciones,3 ilustraciones a color
347			_atext file _bPDF
490	0		_aIntelligent Systems Reference Library _x1868-4394 _v146
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction -- Tools for Study of Pareto Optimal Points -- Some Tools for Decision Tables -- Different Kinds of Decision Trees -- Multi-stage Optimization of Decision Trees with Some Applications -- More Applications of Multi-stage Optimizationof Decision Trees -- Bi-Criteria Optimization Problem for Decision Trees: Cost vs Cost -- Bi-Criteria Optimization Problem for Decision Trees: Cost vs Uncertainty -- Different Kinds of Rules and Systems of Rules.
520	3		_aDynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.
650		7	_aOptimización matemática _9145705 _2embne
700	1		_aAmin, Talha _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aChikalov, Igor _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aHussain, Shahid _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aMoshkov, Mikhail _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783319918389
776	0	8	_iPrinted edition: _z9783319918402
776	0	8	_iPrinted edition: _z9783030063092
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-91839-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _a_alco _a_vill _b10/2019 _cm _dz _ea _feng _ggw _h0