Learning with kernels : support vector machines, regularization, optimization, and beyond /

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks....

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Schölkopf, Bernhard
Otros Autores: Smola, Alexander J.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, ©2002.
Colección:Adaptive computation and machine learning.
Temas:
Machine learning.
Algorithms.
Kernel functions.
Algorithms
Machine Learning
Apprentissage automatique.
Algorithmes.
Noyaux (Mathématiques)
algorithms.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Kernel functions
Machine learning
Machine-learning.
Vectorcomputers.
COMPUTER SCIENCE/Machine Learning & Neural Networks
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Series Foreword; Preface; 1
  • A Tutorial Introduction; I
  • Concepts and Tools; 2
  • Kernels; 3
  • Risk and Loss Functions; 4
  • Regularization; 5
  • Elements of Statistical Learning Theory; 6
  • Optimization; II
  • Support Vector Machines; 7
  • Pattern Recognition; 8
  • Single-Class Problems: Quantile Estimation and Novelty Detection; 9
  • Regression Estimation; 10
  • Implementation; 11
  • Incorporating Invariances; 12
  • Learning Theory Revisited; III
  • Kernel Methods; 13
  • Designing Kernels; 14
  • Kernel Feature Extraction; 15
  • Kernel Fisher Discriminant; 16
  • Bayesian Kernel Methods.
  • 17
  • Regularized Principal Manifolds18
  • Pre-Images and Reduced Set Methods; A
  • Addenda; B
  • Mathematical Prerequisites; References; Index; Notation and Symbols.

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