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Herausgeber: 
  • Johan A.K. Suykens
  • Marco Signoretto
  • Andreas Argyriou
  • Regularization, Optimization, Kernels, and Support Vector Machines 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Oktober 2014  
    Genre:  Naturwissensch., Medizin, Technik 
    ISBN:  9781482241396 
    EAN-Code: 
    9781482241396 
    Verlag:  Taylor and Francis 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 234 mm / B 156 mm / D  
    Gewicht:  1850 gr 
    Seiten:  525 
    Illustration:  Farb., s/w. Abb. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference:

    • Covers the relationship between support vector machines (SVMs) and the Lasso
    • Discusses multi-layer SVMs
    • Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing
    • Describes graph-based regularization methods for single- and multi-task learning
    • Considers regularized methods for dictionary learning and portfolio selection
    • Addresses non-negative matrix factorization
    • Examines low-rank matrix and tensor-based models
    • Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing
    • Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent

    Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

      
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