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International Joint Conference SOCO'16-CISIS'16-ICEUTE'16 : San Sebastián, Spain, October 19th-21st, 2016 Proceedings / Manuel Graña, José Manuel López-Guede, Oier Etxaniz, Álvaro Herrero, Héctor Quintián, Emilio Corchado, editors

By: (11th : SOCO (Conference). jointly held conference ((11th : 2016 : San Sebastián, Spain))
Contributor(s): Corchado, Emilio,, editor literario | Etxaniz, Oier,, editor literario | Graña, Manuel, (1958-), editor literario | Herrero, Alvaro,, editor literario | López-Guede, José Manuel,, editor literario | Quintián, Héctor,, editor literario | ICEUTE (Conference) ((7th :. 2016 :. San Sebastián, Spain)) | International Conference on Complex, Intelligent, and Software Intensive Systems ((9th :. 2016 :. San Sebastián, Spain))
Material type: materialTypeLabelE-bookSeries: (Advances in intelligent systems and computing, 2194-5357 ; 527).Publisher: Cham, Switzerland : Springer, [2016]Copyright date: 2017Description: 1 recurso en línea (xxiv, 805 páginas) : ilustraciones.ISBN: 3319473646; 9783319473642.Subject: Inteligencia artificial -- Congresos y asambleasOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Preface; 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
1.2 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
5 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
Learning 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
Using 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
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería Q334 2017 EB (Browse shelf(Opens below)) Acceso electrónico eBook.20022323
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Includes index

International conference proceedings

SpringerLink

Preface; 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

1.2 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

5 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

Learning 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

Using 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

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