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Empirical Inference : Festschrift in Honor of Vladimir N. Vapnik / / edited by Bernhard Schölkopf, Zhiyuan Luo, Vladimir Vovk
| Empirical Inference : Festschrift in Honor of Vladimir N. Vapnik / / edited by Bernhard Schölkopf, Zhiyuan Luo, Vladimir Vovk |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (xix, 287 pages) : illustrations (some color) |
| Disciplina | 006.31 |
| Collana | Gale eBooks |
| Soggetto topico |
Artificial intelligence
Statistics Computer science - Mathematics Mathematical statistics Mathematical optimization Artificial Intelligence Statistical Theory and Methods Probability and Statistics in Computer Science Optimization |
| ISBN |
9783642411366
3642411363 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I - History of Statistical Learning Theory -- Chap. 1 - In Hindsight: Doklady Akademii Nauk SSSR, 181(4), 1968 -- Chap. 2 - On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities -- Chap. 3 - Early History of Support Vector Machines -- Part II - Theory and Practice of Statistical Learning Theory -- Chap. 4 - Some Remarks on the Statistical Analysis of SVMs and Related Methods -- Chap. 5 - Explaining AdaBoost -- Chap. 6 - On the Relations and Differences Between Popper Dimension, Exclusion Dimension and VC-Dimension -- Chap. 7 - On Learnability, Complexity and Stability -- Chap. 8 - Loss Functions -- Chap. 9 - Statistical Learning Theory in Practice -- Chap. 10 - PAC-Bayesian Theory -- Chap. 11 - Kernel Ridge Regression -- Chap. 12 - Multi-task Learning for Computational Biology: Overview and Outlook -- Chap. 13 - Semi-supervised Learning in Causal and Anticausal Settings -- Chap. 14 - Strong Universal Consistent Estimate of the Minimum Mean-Squared Error -- Chap. 15 - The Median Hypothesis -- Chap. 16 - Efficient Transductive Online Learning via Randomized Rounding -- Chap. 17 - Pivotal Estimation in High-Dimensional Regression via Linear Programming -- Chap. 18 - Some Observations on Sparsity Inducing Regularization Methods for Machine Learning -- Chap. 19 - Sharp Oracle Inequalities in Low Rank Estimation -- Chap. 20 - On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods -- Chap. 21 - Kernels, Pre-images and Optimization -- Chap. 22 - Efficient Learning of Sparse Ranking Functions -- Chap. 23 - Direct Approximation of Divergences Between Probability Distributions -- Index. |
| Record Nr. | UNINA-9910437566303321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Semi-supervised learning / / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien
| Semi-supervised learning / / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, ©2006 |
| Descrizione fisica | 1 online resource (528 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) |
ChapelleOlivier
SchölkopfBernhard ZienAlexander |
| Collana | Adaptive computation and machine learning |
| Soggetto topico | Supervised learning (Machine learning) |
| Soggetto non controllato | COMPUTER SCIENCE/Machine Learning & Neural Networks |
| ISBN |
1-282-09618-4
0-262-25589-8 1-4294-1408-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Series Foreword; Preface; 1 - Introduction to Semi-Supervised Learning; 2 - A Taxonomy for Semi-Supervised Learning Methods; 3 - Semi-Supervised Text Classification Using EM; 4 - Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5 - Probabilistic Semi-Supervised Clustering with Constraints; 6 - Transductive Support Vector Machines; 7 - Semi-Supervised Learning Using Semi- Definite Programming; 8 - Gaussian Processes and the Null-Category Noise Model; 9 - Entropy Regularization; 10 - Data-Dependent Regularization
11 - Label Propagation and Quadratic Criterion12 - The Geometric Basis of Semi-Supervised Learning; 13 - Discrete Regularization; 14 - Semi-Supervised Learning with Conditional Harmonic Mixing; 15 - Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17 - Modifying Distances; 18 - Large-Scale Algorithms; 19 - Semi-Supervised Protein Classification Using Cluster Kernels; 20 - Prediction of Protein Function from Networks; 21 - Analysis of Benchmarks; 22 - An Augmented PAC Model for Semi- Supervised Learning 23 - Metric-Based Approaches for Semi- Supervised Regression and Classification24 - Transductive Inference and Semi-Supervised Learning; 25 - A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index |
| Record Nr. | UNINA-9910777620503321 |
| Cambridge, Mass., : MIT Press, ©2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Semi-supervised learning / / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien
| Semi-supervised learning / / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, c2006 |
| Descrizione fisica | 1 online resource (528 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) |
ChapelleOlivier
SchölkopfBernhard ZienAlexander |
| Collana | Adaptive computation and machine learning |
| Soggetto topico | Supervised learning (Machine learning) |
| ISBN |
1-282-09618-4
0-262-25589-8 1-4294-1408-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Series Foreword; Preface; 1 - Introduction to Semi-Supervised Learning; 2 - A Taxonomy for Semi-Supervised Learning Methods; 3 - Semi-Supervised Text Classification Using EM; 4 - Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5 - Probabilistic Semi-Supervised Clustering with Constraints; 6 - Transductive Support Vector Machines; 7 - Semi-Supervised Learning Using Semi- Definite Programming; 8 - Gaussian Processes and the Null-Category Noise Model; 9 - Entropy Regularization; 10 - Data-Dependent Regularization
11 - Label Propagation and Quadratic Criterion12 - The Geometric Basis of Semi-Supervised Learning; 13 - Discrete Regularization; 14 - Semi-Supervised Learning with Conditional Harmonic Mixing; 15 - Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17 - Modifying Distances; 18 - Large-Scale Algorithms; 19 - Semi-Supervised Protein Classification Using Cluster Kernels; 20 - Prediction of Protein Function from Networks; 21 - Analysis of Benchmarks; 22 - An Augmented PAC Model for Semi- Supervised Learning 23 - Metric-Based Approaches for Semi- Supervised Regression and Classification24 - Transductive Inference and Semi-Supervised Learning; 25 - A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index |
| Record Nr. | UNINA-9910963198603321 |
| Cambridge, Mass., : MIT Press, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||