Machine Learning, Support Vector Machines, and Large Scale Optimization Workshop

Learning interpretable SVMs for biological sequence classification

author: Sören Sonnenburg, Fraunhofer Institute Computer Architecture and Software Technology

Categories

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

You might be experiencing some problems with Your Video player.

*Slides*
0:03	Learning interpretable SVMs for biological
0:32	Roadmap:
0:58	Biology: Detection of Splice Sites
2:09	Approach: String Kernel + SVM
3:45	Success
5:33	Gain
8:20	Reformulation: Multiple Kernel Learning
8:32	Biology: Detection of Splice Sites
9:05	Approach: String Kernel + SVM
9:54	Multiple Kernel Learning
10:47	Constraining the weights
11:57	Standard SVM Optimization Problem
12:09	MKL Optimization Problem I
13:23	MKL Optimization Problem II
15:30	MKL Optimization Problem II
17:59	The Semi-Infinite Linear Program I
18:23	The Semi-Infinite Linear Program II
18:59	Solving the SILP: Column Generation I
21:05	Solving the SILP: Column Generation II
21:38	Solving the SILP: Boosting I
22:59	Solving the SILP: Boosting I
23:21	Stability of the solution ?
24:52	Toy Dataset
28:27	Application to Acceptor splice sites
29:35	Conclusion

Lecture rating

People found this lecture:
Worth seeing
because it is:
Valuable and informative
Well presented
Easily understandable
Acceptably recorded
You need to login to cast your vote.

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.