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008			170227s2017 sz o 101 0 eng d
020			_a3319534807 _q(electronic bk.)
020			_a9783319534800 _q(electronic bk.)
020			_z3319534793
020			_z9783319534794 _q(print)
040			_aN$T _cN$T _dGW5XE _dN$T _dIDEBK _dEBLCP _dYDX _dUAB _dOCLCF _dCOO _dIOG _dAZU _dUWO _dUPM _dESU _dZ5A _dJBG _dIAD _dICW _dICN _dILO _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA76.76.E95 _bI584 2017 EB
111	2		_aInternational Conference on Intelligent Systems Design and Applications _n(16th : _d2016 : _cPorto, Portugal)
245	1	0	_aIntelligent systems design and applications : _b16th International Conference on Intelligent Systems Design and Applications (ISDA 2016) held in Porto, Portugal, December 16-18, 2016 _cAna Maria Madureira, Ajith Abraham, Dorabela Gamboa, Paulo Novais, editors.
246	3		_aISDA 2016
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aAdvances in intelligent systems and computing _x2194-5357 _vvolume 557
500			_aIncluye índice de autor
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aPreface; ISDA 2016 -- Organization; General Chairs; Program Chairs; Advisory Board members; Tutorials/Workshops and Special Sessions Chairs; Publication Chair; Web Master; Local Organizing Committee; Technical Program Committee; Contents; An Innovative Approach to Manage Heterogeneous Information Using Relational Database Systems; Abstract; 1 Introduction; 2 Defining the Concepts Used for Solving this Problem; 2.1 Library Standards for Defining Information; 2.2 Representing Information Structures Using FRBR Model; 3 Problem Solution Using Metadata Definitions; 4 Preliminary Results.
505	8		_a4 Data Domains for the Experiments5 Experiments, Results and Analysis; 6 Conclusions; Acknowledgments; References; Agglomerative and Divisive Approaches to Unsupervised Learning in Gestalt Clusters; Abstract; 1 Introduction; 2 The Agglomerative Approach; 3 The Divisive Approach; 4 Data Domain for the Experiments; 5 Experiments, Results and Analysis; 6 Conclusions; Acknowledgments; References; Improving Imputation Accuracy in Ordinal Data Using Classification; 1 Introduction; 2 Related Work; 3 Classifier Based Nominal Imputation Algorithm (CNI); 4 Experimental Setup; 4.1 Experimental Results.
505	8		_a4 Validation Indices, Experiments, Results and Analysis5 Conclusions; Acknowledgments; References; Historic Document Image De-noising Using Principal Component Analysis (PCA) and Local Pixel Grouping ... ; Abstract; 1 Introduction; 2 Related Work; 3 LPG-PCA De-noising Algorithm; 3.1 Principal Component Analysis (PCA); 3.2 Local Pixel Grouping (LPG); 3.3 LPG-PCA Based De-noising First Stage; 3.4 LPG-PCA Based De-noising Refinement in Second Stage; 4 Experimentation; 4.1 Datasets; 4.2 Assessment Metrics; 4.3 Experimental Result; 5 Conclusion; Acknowledgments; References.
505	8		_a4.2 Classification Accuracy4.3 K Nearest Neighbour Algorithm (KNN-5); 5 Conclusions; References; GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance; Abstract; 1 Introduction; 2 Background; 2.1 GA-FastSLAM Approach; 2.2 PSO-FastSLAM Approach; 3 The Proposed GA-PSO-FastSLAM Approach; 4 Experiment; 4.1 Experiment Setup; 4.2 Experiment Result; 5 Conclusion; Acknowledgment; References; Three Case Studies Using Agglomerative Clustering; Abstract; 1 Introduction; 2 AGNES (AGglomerative NESting) Algorithm; 3 Data Description.
505	8		_a5 ConclusionsReferences; Reliable Attribute Selection Based on Random Forest (RASER); Abstract; 1 Introduction; 2 Related Work; 3 Reliable Attribute Selection Based on Random Forest (RASER); 3.1 Measuring Features Relevance; 3.2 Measuring Redundancy; 3.3 Subgraph Computation; 4 Experimental Results; 4.1 Value of Relevance; 4.2 Performance; 5 Conclusion; References; Estimating the Number of Clusters as a Pre-processing Step to Unsupervised Learning; Abstract; 1 Introduction; 2 Estimating the Number of Clusters of a Clustering by Using a Sequential Clustering Algorithm; 3 The BSAS Algorithm.
520	3		_aThis book comprises selected papers from the 16th International Conference on Intelligent Systems Design and Applications (ISDA?16), which was held in Porto, Portugal from December 1 to16, 2016. ISDA 2016 was jointly organized by the Portugual-based Instituto Superior de Engenharia do Porto and the US-based Machine Intelligence Research Labs (MIR Labs) to serve as a forum for the dissemination of state-of-the-art research and development of intelligent systems, intelligent technologies, and applications. The papers included address a wide variety of themes ranging from theories to applications of intelligent systems and computational intelligence area and provide a valuable resource for students and researchers in academia and industry alike.
650		7	_aInteligencia artificial _2embne _0(OCoLC)fst00817247 _0 _9413115
700	1		_aAbraham, Ajith _d1968- _eeditor literario _945309
700	1		_aGamboa, Dorabela, _eeditor literario
700	1		_aMadureira, Ana _q(Ana Maria), _eeditor literario
700	1		_aNovais, Paulo, _eeditor literario _997754
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-53480-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95447 _d95447 _x1