Home > Algorithme de Classification: Machine Vecteurs de Support, Analyse Discriminante Linaire, Locality Sensitive Hashing, Classifieur Linaire(French)
Algorithme de Classification: Machine Vecteurs de Support, Analyse Discriminante Linaire, Locality Sensitive Hashing, Classifieur Linaire(French)

Algorithme de Classification: Machine Vecteurs de Support, Analyse Discriminante Linaire, Locality Sensitive Hashing, Classifieur Linaire(French)

          
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About the Book

Les achats comprennent une adhsion l'essai gratuite au club de livres de l'diteur, dans lequel vous pouvez choisir parmi plus d'un million d'ouvrages, sans frais. Le livre consiste d'articles Wikipedia sur: Machine Vecteurs de Support, Analyse Discriminante Linaire, Locality Sensitive Hashing, Classifieur Linaire, Perceptron, Algorithme Id3, Boosting, Adaboost, Rankboost, Algorithme C4.5, Mthode Des K Plus Proches Voisins, Algorithme Cart. Non illustr. Mises jour gratuites en ligne. Extrait: Les machines vecteurs de support ou sparateurs vaste marge (en anglais Support Vector Machine, SVM) sont un ensemble de techniques d'apprentissage supervis destines rsoudre des problmes de discrimination et de rgression. Les SVM sont une gnralisation des classifieurs linaires. Les SVM ont t dvelopps dans les annes 1990 partir des considrations thoriques de Vladimir Vapnik sur le dveloppement d'une thorie statistique de l'apprentissage: la Thorie de Vapnik-Chervonenkis. Les SVM ont rapidement t adopts pour leur capacit travailler avec des donnes de grandes dimensions, le faible nombre d'hyper paramtres, le fait qu'ils soient bien fonds thoriquement, et leurs bons rsultats en pratique. Les SVM ont t appliqus de trs nombreux domaines (bio-informatique, recherche d'information, vision par ordinateur, finance...). Selon les donnes, la performance des machines vecteurs de support est de mme ordre, ou mme suprieure, celle d'un rseau de neurones ou d'un modle de mixture gaussienne. Les sparateurs vastes marges reposent sur deux ides cls: la notion de marge maximale et la notion de fonction noyau. Ces deux notions existaient depuis plusieurs annes avant qu'elles ne soient mises en commun pour construire les SVM. L'ide des hyperplans marge maximale a t explore ds 1963 par Vladimir Vapnik et A. Lerner, et en 1973 par Richard Duda et Peter Hart dans leur livre Pattern C...http: //booksllc.net/?l=fr


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Product Details
  • ISBN-13: 9781159626549
  • Publisher: Books LLC
  • Publisher Imprint: Books LLC
  • Height: 152 mm
  • No of Pages: 56
  • Series Title: French
  • Sub Title: Machine Vecteurs de Support, Analyse Discriminante Linaire, Locality Sensitive Hashing, Classifieur Linaire
  • Width: 229 mm
  • ISBN-10: 1159626545
  • Publisher Date: 31 Jul 2010
  • Binding: Paperback
  • Language: French
  • Returnable: N
  • Spine Width: 3 mm
  • Weight: 95 gr


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