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999			_c94718 _d94718 _x1
001			94718
003			ES-MaUEC
005			20240111050131.0
006			m o d
007			cr cnu---unuuu
008			161014s2017 sz a o 101 0 eng d
020			_a3319473646 _q(electronic bk.)
020			_a9783319473642 _q(electronic bk.)
020			_z9783319473635 _q(print)
035			_a(OCoLC)960701960
040			_aGW5XE _cGW5XE _dOCLCO _dEBLCP _dOCLCF _dOCLCQ _dOCLCO _dNOC _dUAB _dN$T _dIOG _dESU _dZ5A _dJBG _dIAD _dICW _dICN _dOTZ _dU3W _dES-MaUEC _bspa
050		4	_aQ334 _b2017 EB
111	2		_aSOCO (Conference) _n(11th : _d2016 : _cSan Sebastián, Spain), _ejointly held conference.
245	1	0	_aInternational Joint Conference SOCO'16-CISIS'16-ICEUTE'16 : _bSan Sebastián, Spain, October 19th-21st, 2016 Proceedings _cManuel Graña, José Manuel López-Guede, Oier Etxaniz, Álvaro Herrero, Héctor Quintián, Emilio Corchado, editors
264		1	_aCham, Switzerland _bSpringer _c[2016]
264		4	_c2017
300			_a1 recurso en línea (xxiv, 805 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 _v527
500			_aIncludes index
500			_aInternational conference proceedings
500			_aSpringerLink
505	0		_aPreface; SOCO 2016; Contents; SOCO 2016: Classification; Predicting 30-Day Emergency Readmission Risk; Abstract; 1 Introduction; 2 Related Work; 3 Materials and Methods; 3.1 Support Vector Machine; 3.2 Random Forest; 4 Results; 4.1 Class Balancing; 4.2 Feature Selection; 5 Conclusions and Future Work; References; Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability; 1 Introduction; 2 Description of the Features; 3 Experimental Results; 3.1 Linear and Quadratic Support Vector Machine; 3.2 Neural Network; 3.3 Discussion; 4 Conclusions; References
505	8		_a1.2 Collection Efficiency2 Materials; 3 Methodology; 3.1 Linear Regression; 3.2 Enhanced Linear Regression; 3.3 Generalized Linear Regression; 3.4 Enhanced Generalized Linear Regression; 3.5 Method of Validation; 3.6 Model Efficiency; 4 Results; 5 Conclusions; References; Time Analysis of Air Pollution in a Spanish Region Through k-means; Abstract; 1 Introduction; 2 Clustering Techniques and Methods; 2.1 Cluster Evaluation Measures; 2.2 k-means Clustering Technique; 3 Real-Life Case Study; 4 Results and Discussion; 5 Conclusions and Future Work; References
505	8		_a5 The Hierarchical Supervised Classifier Learning6 Use Cases and Conclusion; References; SOCO 2016: Machine Learning; Assisting the Diagnosis of Neurodegenerative Disorders Using Principal Component Analysis and TensorFlow; 1 Introduction; 2 Materials and Methods; 2.1 Data Description; 2.2 Feature Extraction Based on Principal Component Analysis; 2.3 Classification Based on TensorFlow; 3 Experiments and Results; 4 Discussion and Conclusions; References; Cyclone Performance Prediction Using Linear Regression Techniques; Abstract; 1 Introduction; 1.1 Operating Principle
505	8		_aLearning Fuzzy Models with a SAX-based Partitioning for Simulated Seizure Recognition1 Introduction; 2 Related Techniques; 2.1 AntMiner+; 2.2 Learning FRBC with ACS; 2.3 Simbolic Aggregation AproXimation; 3 Introducing SAX in the Model Learning; 4 Experiment and Results; 4.1 Materials and Methods; 4.2 Results and Discussion; 5 Conclusion; References; Real Prediction of Elder People Abnormal Situations at Home; 1 Introduction; 2 Dataset Description, Extraction and Reduce Strategy; 3 Modeling the Elders Behaviour; 4 New Raw Data Vectors to Improve Behaviour Modeling
505	8		_aUsing Non-invasive Wearables for Detecting Emotions with Intelligent Agents1 Introduction; 2 State of the Art; 3 Problem Description; 4 System Proposal; 4.1 Data Acquisition Process; 4.2 Emotion Recognition; 4.3 Wristband Prototype; 5 Conclusions and Future Work; References; Impulse Noise Detection in OFDM Communication System Using Machine Learning Ensemble Algorithms; Abstract; 1 Introduction; 2 System Model; 3 Multi-classifiers (Ensembles) Algorithms; 3.1 Bagging; 3.2 Boosting (Bos); 3.3 Random Forest (RF); 3.4 Stacking (Stack); 4 Simulations; 4.1 Simulation Set-up; 4.2 Results Discussion
588	0		_aOnline resource; title from PDF title page (EBSCO, viewed March 31, 2017)
988			_aEBOOK, EBSPRINGER_2017A
650		7	_aInteligencia artificial _2embne _vCongresos y asambleas _9413115
700	1		_aCorchado, Emilio, _eeditor literario
700	1		_aEtxaniz, Oier, _eeditor literario
700	1		_aGraña, Manuel, _d1958- _eeditor literario
700	1		_aHerrero, Alvaro, _eeditor literario
700	1		_aLópez-Guede, José Manuel, _eeditor literario
700	1		_aQuintián, Héctor, _eeditor literario
711	2		_aICEUTE (Conference) _n(7th : _d2016 : _cSan Sebastián, Spain), _jjointly held conference.
711	2		_aInternational Conference on Complex, Intelligent, and Software Intensive Systems _n(9th : _d2016 : _cSan Sebastián, Spain), _jjointly held conference.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-47364-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2018 _dz _e- _zSI