000			03742nam a2200421 i 4500
710	2		_aSpringerLink (Online service) _9106996
999			_c119373 _d119373
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003			ES-MaUEC
005			20230102113945.0
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007			cr nn nnnaamaa
008			200313s2020 gw a s |||| 0|eng d
020			_a9783030417284
024	7		_a10.1007/978-3-030-41728-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA166.2 _b2020 EB
100	1		_aMoshkov, Mikhail _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673658
245	1	0	_aComparative Analysis of Deterministic and Nondeterministic Decision Trees _cby Mikhail Moshkov.
250			_aFirst edition 2020.
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XVI, 297 páginas) _b4 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aIntelligent Systems Reference Library _x1868-4394 _v179
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction -- Basic Definitions and Notation -- Lower Bounds on Complexity of Deterministic Decision Trees for Decision Tables -- Upper Bounds on Complexity and Algorithms for Construction of Deterministic Decision Trees for Decision Tables -- Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables -- Closed Classes of Boolean Functions -- Algorithmic Problems -- Basic Definitions and Notation -- Main Reductions -- Functions on Main Diagonal and Below -- Local Upper Types of Restricted Sccf-Triples -- Bounds Inside Types. .
520	3		_aThis book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. Deterministic decision trees are widely used as classifiers, as a means of knowledge representation, and as algorithms. Nondeterministic (strongly nondeterministic) decision trees can be interpreted as systems of true decision rules that cover all objects (objects from one decision class). This book develops tools for the study of decision trees, including bounds on complexity and algorithms for construction of decision trees for decision tables with many-valued decisions. It considers two approaches to the investigation of decision trees for problems in information systems: local, when decision trees can use only attributes from the problem representation; and global, when decision trees can use arbitrary attributes from the information system. For both approaches, it describes all possible types of relationships among the four parameters considered and discusses the algorithmic problems related to decision tree optimization. The results presented are useful for researchers who apply decision trees and rules to algorithm design and to data analysis, especially those working in rough set theory, test theory and logical analysis of data. This book can also be used as the basis for graduate courses. .
988			_aSpringer_Robotics_31032020
650		7	_2embne _9668434 _aÁrboles (Teoría de grafos)
776	0	8	_iPrinted edition: _z9783030417277
776	0	8	_iPrinted edition: _z9783030417291
776	0	8	_iPrinted edition: _z9783030417307
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-41728-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2020 _dz _ek _zSI