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2022 International Conference on Machine Learning, Control, and Robotics (MLCR) / / Institute of Electrical and Electronics Engineers
| 2022 International Conference on Machine Learning, Control, and Robotics (MLCR) / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : IEEE, , 2022 |
| Descrizione fisica | 1 online resource |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Computational learning theory |
| ISBN | 1-66545-459-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2022 International Conference on Machine Learning, Control, and Robotics |
| Record Nr. | UNISA-996575092603316 |
| Piscataway, New Jersey : , : IEEE, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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2830-2021 : IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning / / Institute of Electrical and Electronics Engineers
| 2830-2021 : IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | New York, NY, USA : , : IEEE, , 2021 |
| Descrizione fisica | 1 online resource (23 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Deep learning (Machine learning) Reinforcement learning Computational learning theory |
| ISBN | 1-5044-7724-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910503501803321 |
| New York, NY, USA : , : IEEE, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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2830-2021 : IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning / / Institute of Electrical and Electronics Engineers
| 2830-2021 : IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | New York, NY, USA : , : IEEE, , 2021 |
| Descrizione fisica | 1 online resource (23 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Deep learning (Machine learning) Reinforcement learning Computational learning theory |
| ISBN | 1-5044-7724-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996574913703316 |
| New York, NY, USA : , : IEEE, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.]
| Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.] |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press |
| Descrizione fisica | 1 online resource (438 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Computational learning theory
Machine learning - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-50590-7
9786610505906 1-4175-1139-7 600-00-0332-3 1-60129-401-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions
9 Extension of the ν-SVM Range for Classification10 Kernels Methods for Text Processing; 11 An Optimization Perspective on Kernel Partial Least Squares Regression; 12 Multiclass Learning with Output Codes; 13 Bayesian Regression and Classification; 14 Bayesian Field Theory: from Likelihood Fields to Hyperfields; 15 Bayesian Smoothing and Information Geometry; 16 Nonparametric Prediction; 17 Recent Advances in Statistical Learning Theory; 18 Neural Networks in Measurement Systems (an engineering view); List of participants; Subject Index; Author Index |
| Record Nr. | UNINA-9910449823103321 |
| Amsterdam ; ; Washington, DC, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.]
| Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.] |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press |
| Descrizione fisica | 1 online resource (438 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Computational learning theory
Machine learning - Mathematical models |
| ISBN |
1-280-50590-7
9786610505906 1-4175-1139-7 600-00-0332-3 1-60129-401-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions
9 Extension of the ν-SVM Range for Classification10 Kernels Methods for Text Processing; 11 An Optimization Perspective on Kernel Partial Least Squares Regression; 12 Multiclass Learning with Output Codes; 13 Bayesian Regression and Classification; 14 Bayesian Field Theory: from Likelihood Fields to Hyperfields; 15 Bayesian Smoothing and Information Geometry; 16 Nonparametric Prediction; 17 Recent Advances in Statistical Learning Theory; 18 Neural Networks in Measurement Systems (an engineering view); List of participants; Subject Index; Author Index |
| Record Nr. | UNINA-9910783421103321 |
| Amsterdam ; ; Washington, DC, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in learning theory : methods, models, and applications / / edited by Johan Suykens ... [et al.]
| Advances in learning theory : methods, models, and applications / / edited by Johan Suykens ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press |
| Descrizione fisica | 1 online resource (438 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Computational learning theory
Machine learning - Mathematical models |
| ISBN |
1-280-50590-7
9786610505906 1-4175-1139-7 600-00-0332-3 1-60129-401-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions
9 Extension of the ν-SVM Range for Classification10 Kernels Methods for Text Processing; 11 An Optimization Perspective on Kernel Partial Least Squares Regression; 12 Multiclass Learning with Output Codes; 13 Bayesian Regression and Classification; 14 Bayesian Field Theory: from Likelihood Fields to Hyperfields; 15 Bayesian Smoothing and Information Geometry; 16 Nonparametric Prediction; 17 Recent Advances in Statistical Learning Theory; 18 Neural Networks in Measurement Systems (an engineering view); List of participants; Subject Index; Author Index |
| Record Nr. | UNINA-9910814308603321 |
| Amsterdam ; ; Washington, DC, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational and robotic models of the hierarchical organization of behavior / / Gianluca Baldassarre, Marco Mirolli, editors
| Computational and robotic models of the hierarchical organization of behavior / / Gianluca Baldassarre, Marco Mirolli, editors |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Heidelberg [Germany] : , : Springer, , 2013 |
| Descrizione fisica | 1 online resource (vi, 358 pages) : illustrations (some color) |
| Disciplina |
004
006.3 150.72 612.8 |
| Collana | Gale eBooks |
| Soggetto topico | Computational learning theory |
| ISBN | 3-642-39875-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chap. 1 - Computational and Robotic Models of the Hierarchical Organization of Behavior: An Overview -- Chap. 2 - Behavioral Hierarchy: Exploration and Representation -- Chap. 3 - Self-organized Functional Hierarchy Through Multiple Timescales: Neurodynamical Accounts for Behavioral Compositionality -- Chap. 4 - Autonomous Representation Learning in a Developing Agent -- Chap. 5 - Hierarchies for Embodied Action Perception -- Chap. 6 - Learning and Coordinating Repertoires of Behaviors with Common Reward: Credit Assignment and Module Activation -- Chap. 7 - Modular, Multimodal Arm Control Models -- Chap. 8 - Generalization and Interference in Human Motor Control -- Chap. 9 - A Developmental Framework for Cumulative Learning Robots -- Chap. 10 - The Hierarchical Accumulation of Knowledge in the Distributed Adaptive Control Architecture -- Chap. 11 - The Hierarchical Organization of Cortical and Basal Ganglia Systems: A Computationally Informed Review and Integrated Hypothesis -- Chap. 12 - Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans -- Chap. 13 - Neural Network Modelling of Hierarchical Motor Function in the Brain -- Chap. 14 - Restoring Purpose in Behavior. |
| Record Nr. | UNINA-9910437571503321 |
| Heidelberg [Germany] : , : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational learning theory and natural learning systems
| Computational learning theory and natural learning systems |
| Pubbl/distr/stampa | Cambridge, Mass. ; ; London, : MIT Press |
| Descrizione fisica | 1 online resource (xxiii, 407 p.) |
| Disciplina | 006.31 |
| Altri autori (Persone) |
GreinerRussell
PetscheThomas HansonStephen José |
| Collana | A Bradford Book |
| Soggetto topico | Computational learning theory |
| Soggetto non controllato | COGNITIVE SCIENCES/General |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910260612803321 |
| Cambridge, Mass. ; ; London, : MIT Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational thinking : a beginner's guide to problem-solving and programming / / Karl Beecher
| Computational thinking : a beginner's guide to problem-solving and programming / / Karl Beecher |
| Autore | Beecher Karl |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Swindon, UK : , : BCS : the Chartered Institute for IT, , [2017] |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Soggetto topico |
Computer science - Study and teaching
Computer programming - Study and teaching Computational learning theory |
| ISBN |
1-5231-1687-0
1-78017-366-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910796534203321 |
Beecher Karl
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| Swindon, UK : , : BCS : the Chartered Institute for IT, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational thinking : a beginner's guide to problem-solving and programming / / Karl Beecher
| Computational thinking : a beginner's guide to problem-solving and programming / / Karl Beecher |
| Autore | Beecher Karl |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Swindon, UK : , : BCS : the Chartered Institute for IT, , [2017] |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Soggetto topico |
Computer science - Study and teaching
Computer programming - Study and teaching Computational learning theory |
| ISBN |
1-5231-1687-0
1-78017-366-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910817760203321 |
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Beecher Karl
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| Swindon, UK : , : BCS : the Chartered Institute for IT, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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