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Machine Learning, Optimization, and Big Data [[electronic resource] ] : Third International Conference, MOD 2017, Volterra, Italy, September 14–17, 2017, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton
| Machine Learning, Optimization, and Big Data [[electronic resource] ] : Third International Conference, MOD 2017, Volterra, Italy, September 14–17, 2017, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XXI, 600 p. 154 illus.) |
| Disciplina | 006.31 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Artificial intelligence Algorithms Data mining Computer science—Mathematics Computer organization Information Systems Applications (incl. Internet) Artificial Intelligence Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery Mathematics of Computing Computer Systems Organization and Communication Networks |
| ISBN | 3-319-72926-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models -- Abstract -- 1 Introduction -- 2 Background -- 2.1 Cellular Networks -- 2.2 Modeling Approach -- 2.3 Framework Overview -- 3 Model Representation Format -- 4 From Reading to Model -- 4.1 Simple Interaction Translation -- 4.2 Translation of Translocation Interaction -- 4.3 Translation of Complexes -- 4.4 Translation of Nested Interactions -- 4.5 Translation of Direct and Indirect Interactions -- 4.6 Translation from Table Reading Output -- 5 Matching Reading and Modeling -- 5.1 Protein Families -- 5.2 Cell Type -- 5.3 Cellular Location -- 5.4 Contradicting Interaction Type -- 5.5 Negative Information -- 6 Case Study -- 7 Conclusion -- References -- Improving Support Vector Machines Performance Using Local Search -- 1 Introduction -- 2 Support Vector Machines -- 3 Iterated Local Search -- 4 Our ILS Method for SVM Parameters Tuning -- 5 Experimental Analysis -- 6 Conclusions and Future Research -- References -- Projective Approximation Based Quasi-Newton Methods -- 1 Introduction -- 2 Preliminaries -- 2.1 Notation Remarks -- 2.2 Quadratic Response Surface Methodology -- 2.3 Quasi-Newton Optimization Methods -- 3 Algorithm Descriptions -- 4 Theoretical Ground -- 5 Modelling -- 6 Conclusion -- A Proofs -- References -- Intra-feature Random Forest Clustering -- Abstract -- 1 Introduction -- 2 The Algorithm -- 3 Performance Evaluation -- 4 Conclusions -- References -- Dolphin Pod Optimization -- 1 Introduction -- 2 Dolphin Pod Optimization -- 3 DPO Setting Parameters -- 4 Performance Metrics -- 5 Numerical Results -- 5.1 Analytical Benchmark Functions -- 5.2 Hull-Form SBD Optimization Problem -- 6 Conclusions and Future Work -- References -- Contraction Clustering (RASTER) -- 1 Introduction -- 2 Problem Description.
2.1 The Clustering Problem -- 2.2 Motivating Use Case -- 2.3 Limitations of Common Clustering Methods -- 3 RASTER -- 3.1 High-Level Description -- 3.2 Tiles and RASTER Clusters -- 3.3 The Algorithm -- 3.4 Parallel RASTER -- 3.5 Generalizing to Higher Dimensions -- 3.6 Minimum Cluster Size in Disadvantageous Grid Layouts -- 4 Results -- 4.1 Ideal Data -- 4.2 Sample Datasets -- 4.3 Empirical Runtime -- 5 Related Work -- 6 Future Work -- References -- Deep Statistical Comparison Applied on Quality Indicators to Compare Multi-objective Stochastic Optimization Algorithms -- 1 Introduction -- 2 Related Work -- 3 Deep Statistical Comparison -- 4 Results and Discussion -- 4.1 Experimental Setup -- 4.2 First Experiment -- 4.3 Second Experiment -- 5 Conclusion -- References -- On the Explicit Use of Enzyme-Substrate Reactions in Metabolic Pathway Analysis -- 1 Introduction -- 1.1 A Nash Equilibrium Approach to Metabolic Pathways -- 1.2 Element Mass Balances and Charge Balancing -- 2 Explicitly Incorporating Enzyme-Substrate Reactions -- 2.1 Enzyme-Substrate Reactions -- 2.2 An Example of Binding and Unbinding Reactions -- 2.3 Multiple Minima from Protein Docking -- 2.4 A Multi-scale Methodology for Including Enzyme-Substrate Reactions -- 2.5 Enzyme Activity -- 3 Numerical Results -- 4 Conclusions -- References -- A Comparative Study on Term Weighting Schemes for Text Classification -- 1 Introduction -- 2 Text Classification -- 3 Classifiers -- 4 Results and Discussion -- 4.1 Experiments -- 4.2 Evaluation -- 4.3 Results -- 5 Conclusion -- References -- Dual Convergence Estimates for a Family of Greedy Algorithms in Banach Spaces -- 1 Introduction -- 2 Greedy Algorithms -- 3 Primal Convergence Results -- 4 Duality Gap and Convergence Result -- 5 Conclusion -- References -- Nonlinear Methods for Design-Space Dimensionality Reduction in Shape Optimization. 1 Introduction -- 2 Dimensionality Reduction Methods -- 2.1 General Definitions and Assumptions -- 2.2 Principal Component Analysis -- 2.3 Kernel Principal Component Analysis -- 2.4 Local Principal Component Analysis -- 2.5 Deep Autoencoders -- 3 Shape Modification of a Destroyer Hull -- 4 Numerical Results -- 4.1 Evaluation Metrics -- 4.2 Evaluation of Design-Space Dimensionality Reduction Capabilities -- 5 Conclusions and Future Work -- References -- A Differential Evolution Algorithm to Develop Strategies for the Iterated Prisoner's Dilemma -- 1 Introduction -- 2 Differential Evolution: A Short Overview -- 3 Prisoner's Dilemma -- 3.1 Iterated PD and Benchmark Strategies -- 4 DE Develops IPD Strategies -- 4.1 The DE Approach with Memory -- 5 IPD Experiments -- 6 Conclusions -- References -- Automatic Creation of a Large and Polished Training Set for Sentiment Analysis on Twitter -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Training Set Creation -- 3.2 Classification -- 3.3 Dataset Pruning -- 4 Results -- 4.1 Test Set -- 4.2 Accuracy -- 5 Conclusion -- References -- Forecasting Natural Gas Flows in Large Networks -- 1 Introduction -- 1.1 Literature Review -- 1.2 The Data Set -- 1.3 Input Features -- 1.4 The Network -- 1.5 Evaluation -- 2 Recurrent Neural Network (RNN) with Design of Experiments (DOE) and Simulated Annealing -- 2.1 The Experiment -- 2.2 Optimal Design with Simulated Annealing -- 3 Recurrent Neural Network (RNN) with Genetic Algorithm (GA) -- 4 Conclusion -- References -- A Differential Evolution Algorithm to Semivectorial Bilevel Problems -- Abstract -- 1 Introduction -- 2 The SVBLP: Optimistic vs. Pessimistic Approaches -- 3 Optimistic and Pessimistic Frontiers -- 4 A Differential Evolution Algorithm for the SVBLP -- 5 Computational Experiment -- 6 Conclusions -- Acknowledgment -- References. Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces -- 1 Introduction -- 2 Related Work on Transfer Learning in BCI -- 2.1 Ensembles -- 2.2 ELGI -- 3 Methodology -- 3.1 P300 Speller Paradigm -- 3.2 Dataset Recordings -- 3.3 Prefiltering -- 3.4 Classifier -- 3.5 Conditions -- 3.6 Compared Algorithms -- 4 Evolved ELGI Ensemble -- 5 Results -- 6 Discussion and Conclusion -- References -- Hybrid Global/Local Derivative-Free Multi-objective Optimization via Deterministic Particle Swarm with Local Linesearch -- 1 Introduction -- 2 Optimization Problem Formulation -- 3 Performance Metrics -- 4 Hybrid Global/Local Deterministic Algorithm -- 4.1 MODPSO -- 4.2 DFMO -- 4.3 MODHA -- 4.4 Algorithm Parameters and Setup -- 5 Numerical Results -- 5.1 Analytical Benchmark Problems -- 5.2 High-Speed Catamaran Optimization -- 6 Conclusions and Future Work -- References -- Artificial Bee Colony Optimization to Reallocate Personnel to Tasks Improving Workplace Safety -- 1 Introduction -- 2 Multi-objective Optimization -- 2.1 Non-dominated Sorting Bee Colony Optimization -- 3 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) -- 4 Worker's Risk Perception and Caution -- 5 Problem Formulation -- 5.1 Objectives -- 5.2 Problem Formulation -- 6 Experiments and Discussion -- 6.1 Dataset -- 6.2 Setup of the Parameters -- 6.3 Optimization Results -- 7 Conclusion -- References -- Multi-objective Genetic Algorithm for Interior Lighting Design -- 1 Introduction -- 2 The Inverse Lighting Problem -- 2.1 Blender as Direct Engine -- 3 Multi-objective Optimization -- 3.1 Previous Related Works -- 3.2 NSGA-II -- 3.3 Fitness Evaluation and Constraint Handling -- 4 The Proposed Strategy -- 5 Results -- 5.1 Art Gallery -- 5.2 Office -- 6 Conclusions -- References. An Elementary Approach to the Problem of Column Selection in a Rectangular Matrix -- 1 Introduction -- 1.1 Historical Background -- 1.2 Our Contribution -- 2 Proof of Theorem 1.5 -- 2.1 Suitable Choice of the Extracted Vectors -- 2.2 Controlling the Individual Eigenvalues -- 2.3 Controlling the Greatest Eigenvalue -- 2.4 Two Simple Examples -- 3 Computational Considerations -- 3.1 A Simple Algorithm -- 3.2 Scalability vs Accuracy -- 3.3 Extracting Representative Images from a Dataset -- 4 Conclusion -- References -- A Simple and Effective Lagrangian-Based Combinatorial Algorithm for S3VMs -- 1 Introduction and Related Work -- 1.1 The Semi-supervised Scenario -- 1.2 Continuous vs Combinatorial Approach -- 2 Lagrangian S3VM -- 2.1 Dealing with Hyper-parameters -- 2.2 Balance Constraint as a Guide -- 2.3 Inductive vs Transductive S3VMs -- 2.4 Method Details -- 3 Experiments -- 3.1 Algorithms -- 3.2 Datasets -- 3.3 Model Selection -- 3.4 Experimental Results -- 3.5 Technical Details -- 4 Conclusion and Remarks -- References -- A Heuristic Based on Fuzzy Inference Systems for Multiobjective IMRT Treatment Planning -- Abstract -- 1 Introduction -- 2 Brief Review of the Literature -- 3 Multiobjective Optimization Problem -- 4 Heuristic Procedure Based on FIS -- 5 Illustration of the Application of the Procedure -- 6 Conclusions -- Acknowledgments -- References -- Data-Driven Machine Learning Approach for Predicting Missing Values in Large Data Sets: A Comparison ... -- Abstract -- 1 Introduction -- 2 Related Work -- 3 System Design -- 3.1 Data Source and Data Preparation -- 3.2 Methods for Imputation of Missing Values -- 4 Performance Measurements and Results -- 4.1 Algorithms Tuning -- 4.2 Evaluation Measures -- 4.3 Results and Considerations -- 5 Proposed Imputation Approach -- 6 Conclusions -- References. Mineral: Multi-modal Network Representation Learning. |
| Record Nr. | UNISA-996465494703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning, Optimization, and Big Data : Third International Conference, MOD 2017, Volterra, Italy, September 14–17, 2017, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton
| Machine Learning, Optimization, and Big Data : Third International Conference, MOD 2017, Volterra, Italy, September 14–17, 2017, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XXI, 600 p. 154 illus.) |
| Disciplina | 006.31 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Artificial intelligence Algorithms Data mining Computer science—Mathematics Computer organization Information Systems Applications (incl. Internet) Artificial Intelligence Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery Mathematics of Computing Computer Systems Organization and Communication Networks |
| ISBN | 3-319-72926-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models -- Abstract -- 1 Introduction -- 2 Background -- 2.1 Cellular Networks -- 2.2 Modeling Approach -- 2.3 Framework Overview -- 3 Model Representation Format -- 4 From Reading to Model -- 4.1 Simple Interaction Translation -- 4.2 Translation of Translocation Interaction -- 4.3 Translation of Complexes -- 4.4 Translation of Nested Interactions -- 4.5 Translation of Direct and Indirect Interactions -- 4.6 Translation from Table Reading Output -- 5 Matching Reading and Modeling -- 5.1 Protein Families -- 5.2 Cell Type -- 5.3 Cellular Location -- 5.4 Contradicting Interaction Type -- 5.5 Negative Information -- 6 Case Study -- 7 Conclusion -- References -- Improving Support Vector Machines Performance Using Local Search -- 1 Introduction -- 2 Support Vector Machines -- 3 Iterated Local Search -- 4 Our ILS Method for SVM Parameters Tuning -- 5 Experimental Analysis -- 6 Conclusions and Future Research -- References -- Projective Approximation Based Quasi-Newton Methods -- 1 Introduction -- 2 Preliminaries -- 2.1 Notation Remarks -- 2.2 Quadratic Response Surface Methodology -- 2.3 Quasi-Newton Optimization Methods -- 3 Algorithm Descriptions -- 4 Theoretical Ground -- 5 Modelling -- 6 Conclusion -- A Proofs -- References -- Intra-feature Random Forest Clustering -- Abstract -- 1 Introduction -- 2 The Algorithm -- 3 Performance Evaluation -- 4 Conclusions -- References -- Dolphin Pod Optimization -- 1 Introduction -- 2 Dolphin Pod Optimization -- 3 DPO Setting Parameters -- 4 Performance Metrics -- 5 Numerical Results -- 5.1 Analytical Benchmark Functions -- 5.2 Hull-Form SBD Optimization Problem -- 6 Conclusions and Future Work -- References -- Contraction Clustering (RASTER) -- 1 Introduction -- 2 Problem Description.
2.1 The Clustering Problem -- 2.2 Motivating Use Case -- 2.3 Limitations of Common Clustering Methods -- 3 RASTER -- 3.1 High-Level Description -- 3.2 Tiles and RASTER Clusters -- 3.3 The Algorithm -- 3.4 Parallel RASTER -- 3.5 Generalizing to Higher Dimensions -- 3.6 Minimum Cluster Size in Disadvantageous Grid Layouts -- 4 Results -- 4.1 Ideal Data -- 4.2 Sample Datasets -- 4.3 Empirical Runtime -- 5 Related Work -- 6 Future Work -- References -- Deep Statistical Comparison Applied on Quality Indicators to Compare Multi-objective Stochastic Optimization Algorithms -- 1 Introduction -- 2 Related Work -- 3 Deep Statistical Comparison -- 4 Results and Discussion -- 4.1 Experimental Setup -- 4.2 First Experiment -- 4.3 Second Experiment -- 5 Conclusion -- References -- On the Explicit Use of Enzyme-Substrate Reactions in Metabolic Pathway Analysis -- 1 Introduction -- 1.1 A Nash Equilibrium Approach to Metabolic Pathways -- 1.2 Element Mass Balances and Charge Balancing -- 2 Explicitly Incorporating Enzyme-Substrate Reactions -- 2.1 Enzyme-Substrate Reactions -- 2.2 An Example of Binding and Unbinding Reactions -- 2.3 Multiple Minima from Protein Docking -- 2.4 A Multi-scale Methodology for Including Enzyme-Substrate Reactions -- 2.5 Enzyme Activity -- 3 Numerical Results -- 4 Conclusions -- References -- A Comparative Study on Term Weighting Schemes for Text Classification -- 1 Introduction -- 2 Text Classification -- 3 Classifiers -- 4 Results and Discussion -- 4.1 Experiments -- 4.2 Evaluation -- 4.3 Results -- 5 Conclusion -- References -- Dual Convergence Estimates for a Family of Greedy Algorithms in Banach Spaces -- 1 Introduction -- 2 Greedy Algorithms -- 3 Primal Convergence Results -- 4 Duality Gap and Convergence Result -- 5 Conclusion -- References -- Nonlinear Methods for Design-Space Dimensionality Reduction in Shape Optimization. 1 Introduction -- 2 Dimensionality Reduction Methods -- 2.1 General Definitions and Assumptions -- 2.2 Principal Component Analysis -- 2.3 Kernel Principal Component Analysis -- 2.4 Local Principal Component Analysis -- 2.5 Deep Autoencoders -- 3 Shape Modification of a Destroyer Hull -- 4 Numerical Results -- 4.1 Evaluation Metrics -- 4.2 Evaluation of Design-Space Dimensionality Reduction Capabilities -- 5 Conclusions and Future Work -- References -- A Differential Evolution Algorithm to Develop Strategies for the Iterated Prisoner's Dilemma -- 1 Introduction -- 2 Differential Evolution: A Short Overview -- 3 Prisoner's Dilemma -- 3.1 Iterated PD and Benchmark Strategies -- 4 DE Develops IPD Strategies -- 4.1 The DE Approach with Memory -- 5 IPD Experiments -- 6 Conclusions -- References -- Automatic Creation of a Large and Polished Training Set for Sentiment Analysis on Twitter -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Training Set Creation -- 3.2 Classification -- 3.3 Dataset Pruning -- 4 Results -- 4.1 Test Set -- 4.2 Accuracy -- 5 Conclusion -- References -- Forecasting Natural Gas Flows in Large Networks -- 1 Introduction -- 1.1 Literature Review -- 1.2 The Data Set -- 1.3 Input Features -- 1.4 The Network -- 1.5 Evaluation -- 2 Recurrent Neural Network (RNN) with Design of Experiments (DOE) and Simulated Annealing -- 2.1 The Experiment -- 2.2 Optimal Design with Simulated Annealing -- 3 Recurrent Neural Network (RNN) with Genetic Algorithm (GA) -- 4 Conclusion -- References -- A Differential Evolution Algorithm to Semivectorial Bilevel Problems -- Abstract -- 1 Introduction -- 2 The SVBLP: Optimistic vs. Pessimistic Approaches -- 3 Optimistic and Pessimistic Frontiers -- 4 A Differential Evolution Algorithm for the SVBLP -- 5 Computational Experiment -- 6 Conclusions -- Acknowledgment -- References. Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces -- 1 Introduction -- 2 Related Work on Transfer Learning in BCI -- 2.1 Ensembles -- 2.2 ELGI -- 3 Methodology -- 3.1 P300 Speller Paradigm -- 3.2 Dataset Recordings -- 3.3 Prefiltering -- 3.4 Classifier -- 3.5 Conditions -- 3.6 Compared Algorithms -- 4 Evolved ELGI Ensemble -- 5 Results -- 6 Discussion and Conclusion -- References -- Hybrid Global/Local Derivative-Free Multi-objective Optimization via Deterministic Particle Swarm with Local Linesearch -- 1 Introduction -- 2 Optimization Problem Formulation -- 3 Performance Metrics -- 4 Hybrid Global/Local Deterministic Algorithm -- 4.1 MODPSO -- 4.2 DFMO -- 4.3 MODHA -- 4.4 Algorithm Parameters and Setup -- 5 Numerical Results -- 5.1 Analytical Benchmark Problems -- 5.2 High-Speed Catamaran Optimization -- 6 Conclusions and Future Work -- References -- Artificial Bee Colony Optimization to Reallocate Personnel to Tasks Improving Workplace Safety -- 1 Introduction -- 2 Multi-objective Optimization -- 2.1 Non-dominated Sorting Bee Colony Optimization -- 3 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) -- 4 Worker's Risk Perception and Caution -- 5 Problem Formulation -- 5.1 Objectives -- 5.2 Problem Formulation -- 6 Experiments and Discussion -- 6.1 Dataset -- 6.2 Setup of the Parameters -- 6.3 Optimization Results -- 7 Conclusion -- References -- Multi-objective Genetic Algorithm for Interior Lighting Design -- 1 Introduction -- 2 The Inverse Lighting Problem -- 2.1 Blender as Direct Engine -- 3 Multi-objective Optimization -- 3.1 Previous Related Works -- 3.2 NSGA-II -- 3.3 Fitness Evaluation and Constraint Handling -- 4 The Proposed Strategy -- 5 Results -- 5.1 Art Gallery -- 5.2 Office -- 6 Conclusions -- References. An Elementary Approach to the Problem of Column Selection in a Rectangular Matrix -- 1 Introduction -- 1.1 Historical Background -- 1.2 Our Contribution -- 2 Proof of Theorem 1.5 -- 2.1 Suitable Choice of the Extracted Vectors -- 2.2 Controlling the Individual Eigenvalues -- 2.3 Controlling the Greatest Eigenvalue -- 2.4 Two Simple Examples -- 3 Computational Considerations -- 3.1 A Simple Algorithm -- 3.2 Scalability vs Accuracy -- 3.3 Extracting Representative Images from a Dataset -- 4 Conclusion -- References -- A Simple and Effective Lagrangian-Based Combinatorial Algorithm for S3VMs -- 1 Introduction and Related Work -- 1.1 The Semi-supervised Scenario -- 1.2 Continuous vs Combinatorial Approach -- 2 Lagrangian S3VM -- 2.1 Dealing with Hyper-parameters -- 2.2 Balance Constraint as a Guide -- 2.3 Inductive vs Transductive S3VMs -- 2.4 Method Details -- 3 Experiments -- 3.1 Algorithms -- 3.2 Datasets -- 3.3 Model Selection -- 3.4 Experimental Results -- 3.5 Technical Details -- 4 Conclusion and Remarks -- References -- A Heuristic Based on Fuzzy Inference Systems for Multiobjective IMRT Treatment Planning -- Abstract -- 1 Introduction -- 2 Brief Review of the Literature -- 3 Multiobjective Optimization Problem -- 4 Heuristic Procedure Based on FIS -- 5 Illustration of the Application of the Procedure -- 6 Conclusions -- Acknowledgments -- References -- Data-Driven Machine Learning Approach for Predicting Missing Values in Large Data Sets: A Comparison ... -- Abstract -- 1 Introduction -- 2 Related Work -- 3 System Design -- 3.1 Data Source and Data Preparation -- 3.2 Methods for Imputation of Missing Values -- 4 Performance Measurements and Results -- 4.1 Algorithms Tuning -- 4.2 Evaluation Measures -- 4.3 Results and Considerations -- 5 Proposed Imputation Approach -- 6 Conclusions -- References. Mineral: Multi-modal Network Representation Learning. |
| Record Nr. | UNINA-9910768197103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning, Optimization, and Big Data [[electronic resource] ] : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers / / edited by Panos M. Pardalos, Piero Conca, Giovanni Giuffrida, Giuseppe Nicosia
| Machine Learning, Optimization, and Big Data [[electronic resource] ] : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers / / edited by Panos M. Pardalos, Piero Conca, Giovanni Giuffrida, Giuseppe Nicosia |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XVIII, 456 p. 109 illus.) |
| Disciplina | 006.31 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Algorithms Computer system failures Artificial intelligence Pattern recognition Data mining Information Systems Applications (incl. Internet) Algorithm Analysis and Problem Complexity System Performance and Evaluation Artificial Intelligence Pattern Recognition Data Mining and Knowledge Discovery |
| ISBN | 3-319-51469-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine Learning -- Feature Selection -- Neural Networks -- Optimization -- Deep Learning -- Data Sciences -- Data Analytics -- Artificial Intelligence. |
| Record Nr. | UNISA-996466082603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning, Optimization, and Big Data : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers / / edited by Panos M. Pardalos, Piero Conca, Giovanni Giuffrida, Giuseppe Nicosia
| Machine Learning, Optimization, and Big Data : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers / / edited by Panos M. Pardalos, Piero Conca, Giovanni Giuffrida, Giuseppe Nicosia |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XVIII, 456 p. 109 illus.) |
| Disciplina | 006.31 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Algorithms Electronic digital computers - Evaluation Artificial intelligence Pattern recognition systems Data mining Computer and Information Systems Applications System Performance and Evaluation Artificial Intelligence Automated Pattern Recognition Data Mining and Knowledge Discovery |
| ISBN | 3-319-51469-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine Learning -- Feature Selection -- Neural Networks -- Optimization -- Deep Learning -- Data Sciences -- Data Analytics -- Artificial Intelligence. |
| Record Nr. | UNINA-9910483016203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning, Optimization, and Data Science [[electronic resource] ] : 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton, Vincenzo Sciacca
| Machine Learning, Optimization, and Data Science [[electronic resource] ] : 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton, Vincenzo Sciacca |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXII, 562 p. 224 illus., 115 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Machine learning Algorithms Data mining Information Systems Applications (incl. Internet) Machine Learning Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery |
| ISBN | 3-030-13709-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine learning -- Artificial intelligence -- Reinforcement learning -- Computational optimization -- Data science presenting -- Deep learning -- Big data -- Data analytics -- Neural networks. |
| Record Nr. | UNISA-996466425003316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning, Optimization, and Data Science [[electronic resource] ] : 5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings / / edited by Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca
| Machine Learning, Optimization, and Data Science [[electronic resource] ] : 5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings / / edited by Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXVI, 772 p. 225 illus., 160 illus. in color.) |
| Disciplina | 005.14 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Artificial intelligence Data mining Information Systems Applications (incl. Internet) Artificial Intelligence Data Mining and Knowledge Discovery |
| ISBN | 3-030-37599-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Neural Network Ensembles -- Driver Distraction Detection Using Deep Neural Network -- Deep Learning Algorithms for Complex Pattern Recognition in Ultrasonic Sensors Arrays -- An Information Analysis Approach into Feature Understanding of Convolutional Deep Neural Networks -- Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks -- Quantitative and Ontology-Based Comparison of Explanations for Image Classification -- About generative aspects of Variational Autoencoders -- Adapted Random Survival Forest for Histograms to Analyze NOx Sensor Failure in Heavy Trucks -- Incoherent submatrix selection via approximate independence sets in scalar product graphs -- LIA: A Label-Independent Algorithm for Feature Selection for Supervised Learning -- Relationship Estimation Metrics for Binary SoC Data -- Network Alignment using Graphlet Signature and High Order Proximity -- Effect of Market Spread over Reinforcement Learning based Market Maker -- A Beam Search for the Longest Common Subsequence Problem Guided by a Novel Approximate Expected Length Calculation -- An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem -- The measure of regular relations recognition applied to the supervised classification task -- Simple and Accurate classifi cation method based on Class Association Rules performs well on well-known datasets -- Analyses of Multi-collection Corpora via Compound Topic Modeling -- Text mining with constrained tensor decomposition -- The induction problem: a machine learning vindication argument -- Geospatial Dimension in Association Rule Mining: The Case Study of the Amazon Charcoal Tree -- On Probabilistic k-Richness of the k-Means Algorithms -- Using clustering for supervised feature selection to detect relevant features -- A Structural Theorem for Center-Based Clustering in High-Dimensional Euclidean Space -- Modification of the k-MXT Algorithm and Its Application to the Geotagged Data Clustering -- CoPASample: A Heuristics based Covariance Preserving Data Augmentation -- Active Matrix Completion for Algorithm Selection -- A Framework for Multi- delity Modeling in Global Optimization Approaches -- Performance Evaluation of Local Surrogate Models in Bilevel Optimization -- BowTie - a deep learning feedforward neural network for sentiment analysis -- To What Extent Can Text Classifiation Help with Making Inferences About Students' Understanding -- Combinatorial Learning in Traffic Management -- Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits -- Designing an Optimal and Resilient iBGP Overlay with extended ORRTD -- GRASP Heuristics for the Stochastic Weighted Graph Fragmentation Problem -- Uniformly Most-Reliable Graphs and Antiholes -- Merging Quality Estimation for Binary Decision Diagrams with Binary Classfi ers -- Directed Acyclic Graph Reconstruction Leveraging Prior Partial Ordering Information -- Learning Scale and Shift-Invariant Dictionary for Sparse Representation -- Robust kernelized Bayesian matrix factorization for video background/foreground separation -- Parameter Optimization of Polynomial Kernel SVM from miniCV -- Analysing the Over t of the auto-sklearn Automated Machine Learning Tool -- A New Baseline for Automated Hyper-Parameter Optimization -- Optimal trade-o between sample size and precision of supervision for the xed effects panel data model -- Restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City -- Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study -- A Chained Neural Network Model for Photovoltaic Power Forecast -- Trading-o Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels -- Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size -- Multi-Task Learning by Pareto Optimality Nicosia -- Vital prognosis of patients in intensive care units using an Ensemble of Bayesian Classifiers -- On the role of hub and orphan genes in the diagnosis of breast invasive carcinoma -- Approximating Probabilistic Constraints for Surgery Scheduling using Neural Networks -- Determining Principal Component Cardinality through the Principle of Minimum Description Length -- Modelling chaotic time series using recursive deep self-organising neural networks -- On Tree-based Methods for Similarity Learning -- Active Learning Approach for Safe Process Parameter Tuning -- Federated Learning of Deep Neural Decision Forests -- Data Anonymization for Privacy aware Machine Learning -- Exploiting Similar Behavior of Users in a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications -- Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions -- Zero-Shot Fashion Products Clustering on Social Image Streams -- Treating Arti cial Neural Net Training as a Nonsmooth Global Optimization Problem. |
| Record Nr. | UNISA-996466225003316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning, Optimization, and Data Science : 5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings / / edited by Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca
| Machine Learning, Optimization, and Data Science : 5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings / / edited by Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXVI, 772 p. 225 illus., 160 illus. in color.) |
| Disciplina |
005.14
006.31 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Artificial intelligence Data mining Information Systems Applications (incl. Internet) Artificial Intelligence Data Mining and Knowledge Discovery |
| ISBN | 3-030-37599-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Neural Network Ensembles -- Driver Distraction Detection Using Deep Neural Network -- Deep Learning Algorithms for Complex Pattern Recognition in Ultrasonic Sensors Arrays -- An Information Analysis Approach into Feature Understanding of Convolutional Deep Neural Networks -- Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks -- Quantitative and Ontology-Based Comparison of Explanations for Image Classification -- About generative aspects of Variational Autoencoders -- Adapted Random Survival Forest for Histograms to Analyze NOx Sensor Failure in Heavy Trucks -- Incoherent submatrix selection via approximate independence sets in scalar product graphs -- LIA: A Label-Independent Algorithm for Feature Selection for Supervised Learning -- Relationship Estimation Metrics for Binary SoC Data -- Network Alignment using Graphlet Signature and High Order Proximity -- Effect of Market Spread over Reinforcement Learning based Market Maker -- A Beam Search for the Longest Common Subsequence Problem Guided by a Novel Approximate Expected Length Calculation -- An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem -- The measure of regular relations recognition applied to the supervised classification task -- Simple and Accurate classifi cation method based on Class Association Rules performs well on well-known datasets -- Analyses of Multi-collection Corpora via Compound Topic Modeling -- Text mining with constrained tensor decomposition -- The induction problem: a machine learning vindication argument -- Geospatial Dimension in Association Rule Mining: The Case Study of the Amazon Charcoal Tree -- On Probabilistic k-Richness of the k-Means Algorithms -- Using clustering for supervised feature selection to detect relevant features -- A Structural Theorem for Center-Based Clustering in High-Dimensional Euclidean Space -- Modification of the k-MXT Algorithm and Its Application to the Geotagged Data Clustering -- CoPASample: A Heuristics based Covariance Preserving Data Augmentation -- Active Matrix Completion for Algorithm Selection -- A Framework for Multi- delity Modeling in Global Optimization Approaches -- Performance Evaluation of Local Surrogate Models in Bilevel Optimization -- BowTie - a deep learning feedforward neural network for sentiment analysis -- To What Extent Can Text Classifiation Help with Making Inferences About Students' Understanding -- Combinatorial Learning in Traffic Management -- Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits -- Designing an Optimal and Resilient iBGP Overlay with extended ORRTD -- GRASP Heuristics for the Stochastic Weighted Graph Fragmentation Problem -- Uniformly Most-Reliable Graphs and Antiholes -- Merging Quality Estimation for Binary Decision Diagrams with Binary Classfi ers -- Directed Acyclic Graph Reconstruction Leveraging Prior Partial Ordering Information -- Learning Scale and Shift-Invariant Dictionary for Sparse Representation -- Robust kernelized Bayesian matrix factorization for video background/foreground separation -- Parameter Optimization of Polynomial Kernel SVM from miniCV -- Analysing the Over t of the auto-sklearn Automated Machine Learning Tool -- A New Baseline for Automated Hyper-Parameter Optimization -- Optimal trade-o between sample size and precision of supervision for the xed effects panel data model -- Restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City -- Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study -- A Chained Neural Network Model for Photovoltaic Power Forecast -- Trading-o Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels -- Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size -- Multi-Task Learning by Pareto Optimality Nicosia -- Vital prognosis of patients in intensive care units using an Ensemble of Bayesian Classifiers -- On the role of hub and orphan genes in the diagnosis of breast invasive carcinoma -- Approximating Probabilistic Constraints for Surgery Scheduling using Neural Networks -- Determining Principal Component Cardinality through the Principle of Minimum Description Length -- Modelling chaotic time series using recursive deep self-organising neural networks -- On Tree-based Methods for Similarity Learning -- Active Learning Approach for Safe Process Parameter Tuning -- Federated Learning of Deep Neural Decision Forests -- Data Anonymization for Privacy aware Machine Learning -- Exploiting Similar Behavior of Users in a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications -- Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions -- Zero-Shot Fashion Products Clustering on Social Image Streams -- Treating Arti cial Neural Net Training as a Nonsmooth Global Optimization Problem. |
| Record Nr. | UNINA-9910370256203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning, Optimization, and Data Science : 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton, Vincenzo Sciacca
| Machine Learning, Optimization, and Data Science : 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton, Vincenzo Sciacca |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXII, 562 p. 224 illus., 115 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Machine learning Algorithms Data mining Information Systems Applications (incl. Internet) Machine Learning Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery |
| ISBN | 3-030-13709-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine learning -- Artificial intelligence -- Reinforcement learning -- Computational optimization -- Data science presenting -- Deep learning -- Big data -- Data analytics -- Neural networks. |
| Record Nr. | UNINA-9910337583303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||