Log-linear models, extensions, and applications
Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitt...
保存先:
| その他の著者: | |
|---|---|
| フォーマット: | Livre numérique |
| 言語: | Anglais |
| 出版事項: |
Cambridge :
MIT Press
2018.
|
| オンライン･アクセス: | ACCES INSA CVL |
| 注記: |
Book Collection (MIT PRESS) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Log-linear models, extensions, and applications |
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| 245 | 1 | 0 | |a Log-linear models, extensions, and applications |c edited by Aleksandr Aravkin [and six others]. |
| 260 | |a Cambridge : |b MIT Press, |c 2018. | ||
| 300 | |a 1 ressource en ligne (214 pages). | ||
| 500 | |a Book Collection (MIT PRESS) | ||
| 504 | |a Bibliogr. en fin de chapitres | ||
| 520 | |a Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, cover the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications for speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives | ||
| 700 | 1 | |a Aravkin, Aleksandr. |4 edt | |
| 776 | 0 | |t Log-linear models, extensions, and applications | |
| 856 | 4 | |5 180339901:686718283 |u https://ezproxy.insa-cvl.fr:2092/10.7551/mitpress/10012.001.0001 |z ACCES INSA CVL | |
| 997 | |0 841030 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||