Material Detail
A Fast Method for Training Linear SVM in the Primal
This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Antwerp 2008. We propose a new algorithm for training a linear Support Vector Machine in the primal. The algorithm mixes ideas from non smooth optimization, subgradient methods, and cutting planes methods. This yields a fast algorithm that compares well to state of the art algorithms. It is proved to require $O(1/{lambdaepsilon})$ iterations to converge to a solution with accuracy $epsilon$. Additionally we provide an exact shrinking method in the primal that allows reducing the complexity of an iteration to much less than $O(N)$ where $N$ is the number of training samples.
Quality
- User Rating
- Comments
- Learning Exercises
- Bookmark Collections
- Course ePortfolios
- Accessibility Info
More about this material
- Mobile Compatibility:
- Not specified at this time
- Language:
- English
- Cost Involved:
- No
- Source Code Available:
- No
- Accessibility Information Available:
- Unknown
- Creative Commons:
-
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
