OPEN HOUSE on Multi-Task and Complex Outputs Learning

How to Teach Support Vector Machine to Learn Vector Outputs

author: Sandor Szedmak, University of Southampton

Categories

Top: Computer Science: Machine Learning: Kernel Methods: Support Vector Machines

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*Slides*
0:02	How to teach Support Vector Machine
1:04	The “Classical” Support Vector Machine(SVM)
1:08	Reinterpretation of the normal vector w
1:59	Affine transformation = Linear transformation
2:02	Primal problem
4:14	Dual problem
5:27	Primal problem
6:19	Solution
7:16	Prediction
7:28	Prediction when the labels are implicit
8:03	Representation of multiclass output
8:15	Vertices of hyper-tetrahedron
8:56	Embedding Hierarchy
9:45	Methods
10:22	WIPO-alpha dataset
11:05	Computational times
12:20	Multiview learning
13:22	Multiview learning
14:22	Primal problem
15:09	One-class SVM interpretation
15:33	Primal problem
16:07	Spherical embedding
18:52	Embedding Hierarchy
19:23	Similarities in a hierarchy
19:32	Embedding Hierarchy
20:37	WIPO-alpha dataset
21:42	Primal problem
23:57	To get rid of occurrences of explicit labels ...
24:12	Prediction when the labels are implicit

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