Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions - Yingjie Tian,Chunhua Zhang,Naiyang Deng
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Enabling a sound understanding of SVMs, this book gives readers the tools to solve real-world problems using SVMs. It presents an accessible treatment of the two main components of SVMs-classification problems and regression problems. The authors emphasize the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. They construct SVMs for semi- ... Pilns apraksts
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Aprašymas
Enabling a sound understanding of SVMs, this book gives readers the tools to solve real-world problems using SVMs. It presents an accessible treatment of the two main components of SVMs-classification problems and regression problems. The authors emphasize the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. They construct SVMs for semi-supervised, knowledge-based, and robust classification problems. They also cover SVMs for Universum, privileged, multi-class, multi-instance, and multi-label classification problems.
Vairāk informācijas
| Autors | Yingjie Tian, Chunhua Zhang, Naiyang Deng |
|---|---|
| Izdevējs | CRC Press |
| Izlaides gads | 2012 |
| Vāka tips | Cietais vāks |
| EAN | 9781439857922 |