A comprehensive introduction to Support Vector Machines and related kernel methods. 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. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Il n'y a pas encore de discussion sur ce livre
Soyez le premier à en lancer une !
Des romans policiers à offrir ? Faites le plein de bonnes idées !
Nostalgique, nomade ou plutôt romantique ? Trouvez le livre de la rentrée qui vous correspond !
Nouveaux talents, nouveaux horizons littéraires !
Des romans, livres de recettes et BD pour se régaler en famille !
