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020			_a9783319937526
024	7		_a10.1007/978-3-319-93752-6 _2doi
040			_bspa _aES-MaUEC _cES-MaUEC
050		4	_aQA402.5 _b2019 EB
100	1		_aKozak, Jan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671186
245	1	0	_aDecision Tree and Ensemble Learning Based on Ant Colony Optimization _cby Jan Kozak.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019.
300			_a1 recurso en línea (XI, 159 páginas) _b44 ilustraciones
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _v781
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aTheoretical Framework -- Evolutionary Computing Techniques in Data Mining -- Ant Colony Decision Tree Approach -- Adaptive Goal Function of the ACDT Algorithm -- Examples of Practical Application.
520	3		_aThis book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.
650		7	_aOptimización matemática _9145705 _2embne
776	0	8	_iPrinted edition: _z9783319937519
776	0	8	_iPrinted edition: _z9783319937533
776	0	8	_iPrinted edition: _z9783030067168
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-93752-6 _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