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3652.1-2020 - IEEE guide for architectural framework and application of federated machine learning / / IEEE
| 3652.1-2020 - IEEE guide for architectural framework and application of federated machine learning / / IEEE |
| Pubbl/distr/stampa | [Place of publication not identified] : , : IEEE, , 2021 |
| Descrizione fisica | 1 online resource |
| Disciplina | 006.3 |
| Soggetto topico |
Computational intelligence - Simulation methods
Machine learning - Mathematical models |
| ISBN | 1-5044-7053-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910445559703321 |
| [Place of publication not identified] : , : IEEE, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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3652.1-2020 - IEEE guide for architectural framework and application of federated machine learning / / IEEE
| 3652.1-2020 - IEEE guide for architectural framework and application of federated machine learning / / IEEE |
| Pubbl/distr/stampa | [Place of publication not identified] : , : IEEE, , 2021 |
| Descrizione fisica | 1 online resource |
| Disciplina | 006.3 |
| Soggetto topico |
Computational intelligence - Simulation methods
Machine learning - Mathematical models |
| ISBN | 1-5044-7053-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996574668003316 |
| [Place of publication not identified] : , : 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 | ||
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Dataset shift in machine learning / / [edited by] Joaquin Quinonero-Candela ... [et al.]
| Dataset shift in machine learning / / [edited by] Joaquin Quinonero-Candela ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, c2009 |
| Descrizione fisica | 1 online resource (246 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | Quinonero-CandelaJoaquin |
| Collana | Neural information processing series |
| Soggetto topico |
Machine learning
Machine learning - Mathematical models |
| ISBN |
9780262292535
026229253X 9781282240384 1282240382 9780262255103 0262255103 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Series Foreword; Preface; I - Introduction to Dataset Shift; 1 - When Training and Test Sets Are Different: Characterizing Learning Transfer; 2 - Projection and Projectability; II - Theoretical Views on Dataset and Covariate Shift; 3 - Binary Classi cation under Sample Selection Bias; 4 - On Bayesian Transduction: Implications for the Covariate Shift Problem; 5 - On the Training/Test Distributions Gap: A Data Representation Learning Framework; III - Algorithms for Covariate Shift; 6 - Geometry of Covariate Shift with Applications to Active Learning
7 - A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift 8 - Covariate Shift by Kernel Mean Matching; 9 - Discriminative Learning under Covariate Shift with a Single Optimization Problem; 10 - An Adversarial View of Covariate Shift and a Minimax Approach; IV - Discussion; 11 - Author Comments; References; Notation and Symbols; Contributors; Index |
| Record Nr. | UNINA-9910954803903321 |
| Cambridge, Mass., : MIT Press, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Gaussian processes for machine learning [[electronic resource] /] / Carl Edward Rasmussen, Christopher K.I. Williams
| Gaussian processes for machine learning [[electronic resource] /] / Carl Edward Rasmussen, Christopher K.I. Williams |
| Autore | Rasmussen Carl Edward |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, c2006 |
| Descrizione fisica | xviii, 248 p. : ill |
| Disciplina | 519.2/3 |
| Altri autori (Persone) | WilliamsChristopher K. I |
| Collana | Adaptive computation and machine learning |
| Soggetto topico |
Gaussian processes - Data processing
Machine learning - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-262-26107-3
9786612097966 1-4237-6990-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910451997003321 |
Rasmussen Carl Edward
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| Cambridge, Mass., : MIT Press, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Gaussian processes for machine learning / / Carl Edward Rasmussen, Christopher K.I. Williams
| Gaussian processes for machine learning / / Carl Edward Rasmussen, Christopher K.I. Williams |
| Autore | Rasmussen Carl Edward |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, c2006 |
| Descrizione fisica | xviii, 248 p. : ill |
| Disciplina | 519.2/3 |
| Altri autori (Persone) | WilliamsChristopher K. I |
| Collana | Adaptive computation and machine learning |
| Soggetto topico |
Gaussian processes - Data processing
Machine learning - Mathematical models |
| ISBN |
0-262-26107-3
9786612097966 1-4237-6990-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- Series Foreword -- Preface -- Symbols and Notation -- Chapter 1 Introduction -- Chapter 2 Regression -- Chapter 3 Classification -- Chapter 4 Covariance functions -- Chapter 5 Model Selection and Adaptation of Hyperparameters -- Chapter 6 Relationships between GPs and Other Models -- Chapter 7 Theoretical Perspectives -- Chapter 8 Approximation Methods for Large Datasets -- Chapter 9 Further Issues and Conclusions -- Appendix A Mathematical Background -- Appendix B Gaussian Markov Processes -- Appendix C Datasets and Code -- Bibliography -- Author Index -- Subject Index. |
| Record Nr. | UNINA-9910666795503321 |
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Rasmussen Carl Edward
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| Cambridge, Mass., : MIT Press, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Interpreting machine learning models : learn model interpretability and explainability methods / / Anirban Nandi and Aditya Kumar Pal
| Interpreting machine learning models : learn model interpretability and explainability methods / / Anirban Nandi and Aditya Kumar Pal |
| Autore | Nandi Anirban |
| Pubbl/distr/stampa | New York, New York : , : Apress, , [2022] |
| Descrizione fisica | 1 online resource (355 pages) |
| Disciplina | 006.31 |
| Soggetto topico | Machine learning - Mathematical models |
| ISBN |
1-5231-5100-5
1-4842-7802-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: The Evolution of Machine Learning -- Chapter 2: Introduction to Model interpretability. -- Chapter 3: Machine Learning Interpretability Taxonomy -- Chapter 4: Common Properties of Explanations Generated by Interpretability Methods -- Chapter 5: Human Factors in Model Interpretability -- Chapter 6: Explainability Facts: A Framework for Systematic Assessment of Explainable Approaches -- Chapter 7: Interpretable ML and Explainable ML Differences -- Chapter 8: Framework of Model Explanations -- Chapter 9: Feature Importance methods -- Details and usage examples -- Chapter 10: Detailing rule-based methods -- Chapter 11: Detailing Counterfactual Methods -- Chapter 12: Detailing Image interpretability methods -- Chapter 13: Explaining text classification models -- Chapter 14: Role of Data in Interpretability -- Chapter 15: The 8 pitfalls of explainability methods -- Conclusion. -- References. |
| Record Nr. | UNINA-9910522997303321 |
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Nandi Anirban
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| New York, New York : , : Apress, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras / / Armando Fandango
| Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras / / Armando Fandango |
| Autore | Fandango Armando |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (474 pages) |
| Disciplina | 006.31 |
| Soggetto topico | Machine learning - Mathematical models |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910794628603321 |
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Fandango Armando
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| Birmingham, England : , : Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras / / Armando Fandango
| Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras / / Armando Fandango |
| Autore | Fandango Armando |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (474 pages) |
| Disciplina | 006.31 |
| Soggetto topico | Machine learning - Mathematical models |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910825995703321 |
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Fandango Armando
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| Birmingham, England : , : Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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