Titre : | Machine learning for data streams : with practical examples in MOA |
Auteurs : | G. Holmes |
Type de document : | ouvrage |
Editeur : | The MIT Press, 2017 |
Collection : | Adaptive computation and machine learning |
ISBN/ISSN/EAN : | 978-0-262-03779-2 |
Format : | 1 vol. (XXI-262 p.) / ill. / 24 cm |
Note générale : | Bibliogr. p. 239-255 |
Langues: | = Anglais |
Mots-clés: | EXPLORATION DE DONNEES ; CLASSIFICATION ; FLUX DE DONNEE ; DETECTION ; AGREGAT ; REGRESSION STATISTIQUE ; LOGICIEL MOA |
Résumé : | Today many information sources--including sensor networks, financial markets, social networks, and healthcare monitoring--are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. -- Provided by publisher |
Exemplaires (1)
Centre | Localisation | Section | Cote | Statut | Disponibilité | Département |
---|---|---|---|---|---|---|
PACA | Biostatistique et Processus Spatiaux | Ouvrages | BM-AV IA043 | Consultable sur place | Exclu du prêt |