000			05042cam a2200457Ii 4500
001			94904
003			ES-MaUEC
005			20240111050132.0
006			m o d
007			cr cnu|||unuuu
008			161122t20162017sz ob 100 0 eng d
020			_a3319462008 _q(electronic bk.)
020			_a9783319462004 _q(electronic bk.)
020			_z9783319461991 _q(print)
035			_a(OCoLC)963932210 _z(OCoLC)966595695
040			_aN$T _cN$T _dIDEBK _dEBLCP _dGW5XE _dYDX _dOCLCF _dN$T _dIDB _dUAB _dIOG _dMERER _dESU _dZ5A _dOCLCQ _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQ334 _b.A383 2016 EB
111	2		_aInternational Workshop on Combinations of Intelligent Methods and Applications _n(5th : _d2015 : _cVietri sul Mare, Italy)
245	1	0	_aAdvances in combining intelligent methods : _bpostproceedings of the 5th International Workshop CIMA-2015, Vietri sul Mare, Italy, November 2015 (at ICTAI 2015) _cIoannis Hatzilygeroudis, Vasile Palade, Jim Prentzas, editors.
264		1	_aCham, Switzerland _bSpringer _c[2016]
264		4	_c2017
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aIntelligent systems reference library _x1868-4394 _vvolume 116
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aPreface; Reviewers (CIMA 2015 Program Committee); Contents; 1 Real-Time Investors' Sentiment Analysis from Newspaper Articles; Abstract; 1.1 Introduction; 1.2 Background; 1.2.1 Framing Effects; 1.2.2 Investor Sentiment Proxy Construction; 1.3 Related Work; 1.4 News Articles Classification Methodology and Sources; 1.4.1 News Sources and Preprocessing; 1.4.2 Classification Methodology; 1.5 Results and Discussion; 1.6 Conclusions and Future Work; Acknowledgments; References; 2 On the Effect of Adding Nodes to TSP Instances: An Empirical Analysis; Abstract; 2.1 Introduction.
505	8		_a2.2 TSP-The Problem and Its Variants2.2.1 The Traveling Salesman Problem-Variants and Their Complexity; 2.2.2 The Traveling Salesman Problem-Approaches; 2.2.3 Benchmarks for TSP; 2.3 Computational Experiment Methodology and Implementation; 2.4 Results and Discussion; 2.5 Conclusions and Future Work; Acknowledgments; References; 3 Comparing Algorithmic Principles for Fuzzy Graph Communities over Neo4j; 3.1 Introduction; 3.2 Related Work; 3.3 Fuzzy Graphs; 3.3.1 Definitions; 3.3.2 Weight Distributions; 3.3.3 Elementary Quality Metrics of Fuzzy Graphs; 3.3.4 Higher Order Data; 3.4 Fuzzy Walktrap.
505	8		_a3.5 Fuzzy Newman-Girvan3.6 Termination Criteria and Clustering Evaluation; 3.7 Source Code; 3.8 Results; 3.8.1 Data Summary; 3.8.2 Analysis; 3.9 Conclusions and Future Work; References; 4 Difficulty Estimation of Exercises on Tree-Based Search Algorithms Using Neuro-Fuzzy and Neuro-Symbolic Approaches; Abstract; 4.1 Introduction; 4.2 Motivation and Background; 4.2.1 Motivation; 4.2.2 Exercises on Search Algorithms; 4.3 Related Work; 4.4 Neuro-Fuzzy and Neurule-Based Approaches for Exercise Difficulty Estimation; 4.4.1 Exercise Analysis and Feature Extraction; 4.4.2 Neuro Fuzzy Approach.
505	8		_a4.4.3 Neurule-Based Approach4.5 Experimental Evaluation; 4.6 Conclusions; Acknowledgment; References; 5 Generation and Nonlinear Mapping of Reducts-Nearest Neighbor Classification; Abstract; 5.1 Introduction; 5.2 Generation of Reducts Based on Nearest Neighbor Relation; 5.2.1 Generation of Reducts Based on Nearest Neighbor Relation with Minimal Distance; 5.2.2 Modified Reduct Based on Reducts; 5.3 Linearly Separable Condition in Data Vector Space; 5.4 Nonlinear Mapping of Reducts Based on Nearest Neighbor Relation; 5.4.1 Generation of Independent Vectors Based on Nearest Neighbor Relation.
505	8		_a5.4.2 Characterized Equation of Nearest Neighbor Relation for Classification5.4.3 Data Characterization on Nearest Neighbor Relation; 5.4.4 Making Boundary Margin; 5.5 Nonlinear Embedding of Reducts and Threshold Element; 5.6 Conclusion; References; 6 New Quality Indexes for Optimal Clustering Model Identification Based on Cross-Domain Approach; 6.1 Introduction; 6.2 Feature Maximization for Feature Selection; 6.3 Experimental Data and Process; 6.4 Results; 6.5 Conclusion; References; 7 A Hybrid User and Item Based Collaborative Filtering Approach by Possibilistic Similarity Fusion.
650		7	_aInteligencia artificial _2embne _0(OCoLC)fst00817247 _0 _9413115
700	1		_aHatzilygeroudis, Ioannis _eeditor literario _998065
700	1		_aPalade, Vasile, _d1964- _eeditor literario _998066
700	1		_aPrentzas, Jim, _eeditor literario _998067
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-46200-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017A
998			_b02/2018 _dz _e- _zSI
999			_c94904 _d94904 _x1