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...
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| Format: | eBook |
| Language: | English |
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Cambridge
MIT Press
2018
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| Series: | Neural information processing series
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| Collection: | MIT Press eBook Archive - Collection details see MPG.ReNa |
| Summary: | 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 |
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| Physical Description: | 214 pages |
| ISBN: | 0262351609 9780262351607 |