000			04932cam a2200469Ii 4500
001			94840
003			ES-MaUEC
005			20230102112634.0
006			m o d
007			cr cnu|||unuuu
008			161109t20162017sz a o 101 0 eng d
020			_a3319489445 _q(electronic bk.)
020			_a9783319489445 _q(electronic bk.)
020			_z3319489437
020			_z9783319489438 _q(print)
035			_a(OCoLC)962303228 _z(OCoLC)962324894 _z(OCoLC)965534760
040			_aN$T _cN$T _dIDEBK _dEBLCP _dGW5XE _dN$T _dYDX _dOCLCF _dUAB _dIOG _dMERER _dESU _dZ5A _dOCLCQ _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aTA168 _b.A383 2016 EB
111	2		_aInternational Conference on Systems Science _n(19th : _d2016 : _cWrocław, Poland)
245	1	0	_aAdvances in systems science : _bproceedings of the International Conference on Systems Science 2016 (ICSS 2016) _cJerzy Świątek, Jakub M. Tomczak, editors.
246	3		_aICSS 2016
264		1	_aCham, Switzerland _bSpringer _c[2016]
264		4	_c2017
300			_a1 recurso en línea (xi, 340 páginas) _bilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdvances in intelligent systems and computing _x2194-5357 _vvolume 539
500			_aIncludes author index.
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aPreface; Contents; Applications of Machine Learning; Maximum Likelihood Estimation and Optimal Coordinates; 1 Introduction; 2 Entropy and Gaussian Random Variables; 3 Rescaling; 4 Main Result; 5 Conclusion; References; Domain Adaptation for Image Analysis: An Unsupervised Approach Using Boltzmann Machines Trained by Perturbation; 1 Introduction; 2 Unsupervised Domain Adaptation Problem; 3 Boltzmann Machines; 4 Perturb-and-MAP Learning Algorithm; 5 Experiments; 6 Discussion and Conclusion; References; Relation Recognition Problems and Algebraic Approach to Their Solution; Abstract.
505	8		_a1 Introduction2 Basic Notions; 3 Solution of Simple and Extended RR Problems; 4 Solution of Matching RR Problems; 5 Solution of Constructive RR Problems; 6 The CRR Problem; 7 Conclusions; Acknowledgement; References; Prediction of Power Load Demand Using Modified Dynamic Weighted Majority Method; Abstract; 1 Introduction; 2 Related Work; 3 Proposed Method; 3.1 Modified Dynamic Weighted Majority Method; 3.2 Dynamic Weighted Majority with Decomposition; 3.3 Technical Issues; 3.4 Optimization of Parameters by Particle Swarm Optimization; 4 Evaluation; 4.1 Data Description; 5 Conclusion.
505	8		_a3.1 Segmentation of Brain in MRI Images3.2 Volume Reconstruction; 3.3 Automatic Editing of CT Images; 4 Conclusion; References; Automated Processing of Micro-CT Scans Using Descriptor-Based Registration of 3D Images; 1 Introduction; 2 Problem Statement; 3 Materials and Methods; 4 Results and Discussion; 5 Conclusion; References; Gender Recognition Based on Speaker's Voice Analysis; 1 Introduction; 2 Algorithm Description; 3 Summary and Conclusions; References; Topic Modeling Based on Frequent Sequences Graphs; 1 Introduction; 2 Topic Modeling Methods; 3 Methodology; 3.1 Method Overview.
505	8		_a3.2 Building Frequent N-grams3.3 Finding Significant Edges; 3.4 Topic Modeling; 4 Experiment Results and Discussion; 5 Concluding Remarks and Future Research; References; Gaussian Process Regression with Categorical Inputs for Predicting the Blood Glucose Level; 1 Introduction; 2 Methodology; 2.1 Gaussian Process Regression Model; 2.2 Prediction; 2.3 Learning; 3 Experiment; 3.1 Data Description; 3.2 Experiment Details; 3.3 Results and Discussion; 4 Conclusion; References; Automated Information Extraction and Classification of Matrix-Based Questionnaire Data; 1 Introduction.
505	8		_aAcknowledgementsReferences; Estimating Cluster Population; Abstract; 1 Introduction; 2 Review of Existing Approaches; 3 Estimation of Cluster Population; 3.1 Preliminaries; 3.2 Adaptive Bucketing; 3.3 Aggregating Bucket Clusters; 3.4 Nudging; 4 Results and Discussion; References; Evaluation of Particle Swarm Optimisation for Medical Image Segmentation; 1 Introduction; 2 Overview of PSO-Based Algorithms; 2.1 Particle Swarm Optimisation (PSO); 2.2 Darwinian Particle Swarm Optimisation (DPSO); 2.3 Fractional Order Darwinian Particle Swarm Optimisation (FODPSO); 3 Experimental Work.
650		7	_9145606 _aControl, Teoría de _2fast _0(OCoLC)fst00877085 _0
700	1		_aŚwiątek, Jerzy, _eeditor literario _998449
700	1		_aTomczak, Jakub M., _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-48944-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017A
998			_b02/2018 _dz _e- _zSI
999			_c94840 _d94840 _x1