Téléchargez le livre : Machine Learning Paradigms

Machine Learning Paradigms

Applications in Recommender Systems de

,

Éditeur :

Springer


Paru le : 2015-06-13

eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
94,94 €
Téléchargement immédiat
Dès validation de votre commande
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

badge apple Application Louise Reader
badge Android Application Louise Reader
Description

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems.
The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.
Pages
125 pages
Collection
n.c
Parution
2015-06-13
Marque
Springer
EAN papier
9783319191348
EAN PDF
9783319191355
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
12
Taille du fichier
4772 Ko
Prix
94,94 €
EAN EPUB
9783319191355
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
12
Taille du fichier
1690 Ko
Prix
94,94 €

Suggestions personnalisées