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Lawrence Saul
...algorithms ...least squares approach. We present illustrative experimental results and describe efficient implementations for large-scale problems of interest (e.g., with tens of thousands of ...
24m 19s |
a year ago
Videolectures.net
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Andreas Christmann
...regression ...least ...m convex risk minimization applicable for huge data sets for which currently available algorithms are much to slow. As an example we use a data set from 15 German insurance ...
a year ago
Videolectures.net
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...algorithms and proving loss bounds for them. The goal of such algorithms is to perform almost as well as the best decision rules in a wide benchmark class, with no assumptions made about the way ...
2 years ago
Videolectures.net
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...regression ...least ...m convex risk minimization applicable for huge data sets for which currently available algorithms are much to slow. As an example we use a data set from 15 German insurance ...
2 years ago
Videolectures.net
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1396
...regression Instructors: Prof. Eric Grimson, Prof. John Guttag View the complete course at: http://ocw.mit.edu/6-00F08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/...
53m 48s |
3 months ago
YouTube
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Massachusetts Institute of Technology
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We will try to give an elementary account of bounds for "regularized least squares" that reflects our current knowledge. The framework for the discussion is that of Reproducing Kernel Hilbert Spaces, with a regression point of view. As a corollary of these ideas we will see some estimates for the binary classification problem. The talk will be based on joint work with Felipe Cucker and Ding-Xuan? Zhou.
2 years ago
Videolectures.net
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Zhou Xuan
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