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Support vector machines
| Content Provider | Scilit |
|---|---|
| Author | Coqueret, Guillaume Guida, Tony |
| Copyright Year | 2020 |
| Description | While the origins of support vector machines (SVMs) are old (and go back to Vapnik and Lerner (1963)), their modern treatment was initiated in Boser et al. (1992) and Cortes and Vapnik (1995) (binary classification) and Drucker et al. (1997) (regression). We refer to http://www.kernel-machines.org/books for an exhaustive bibliography on their theoretical and empirical properties. SVMs have been very popular since their creation among the machine learning community. Nonetheless, other tools (neural networks especially) have gained popularity and progressively replaced SVMs in many applications like computer vision notably. Book Name: Machine Learning for Factor Investing |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-11387-8&isbn=9781003034858&doi=10.1201/9781003034858-10&format=pdf |
| Ending Page | 128 |
| Page Count | 6 |
| Starting Page | 123 |
| DOI | 10.1201/9781003034858-10 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-08-05 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Machine Learning for Factor Investing Hardware and Architecturee Vector Machines |
| Content Type | Text |
| Resource Type | Chapter |
