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Data-Dependent Geometries and Structures: Analyses and Algorithms for Machine Learning
Published on Apr 25, 20122982 Views
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Chapter list
Data-Dependent Geometries and Structures : Analyses and Algorithms for Machine Learning00:00
Data Dependent Geometry - 100:37
Data Dependent Geometry - 201:24
Example: Consider the following dataset of a new stories - 102:01
Example: Consider the following dataset of a new stories - 202:51
Example: Consider the following dataset of a new stories - 303:18
Example: Consider the following dataset of a new stories - 403:33
Illustration - 104:13
Illustration - 204:56
Data-dependent Geometry05:27
Resources Allocated08:19
Outputs08:51
Main Insubria activities09:30
Vertex Classification: The Shazoo algorithm11:45
Shazoo Algortihm: Analysis, implementation and computational complexity15:05
Shazoo algorithm: Experiments15:55
Link classification18:34
Active link classification: Main results21:03
UCL Activities (Part I)23:04
A triangle inequality for p-resistance - 124:33
A triangle inequality for p-resistance - 226:06
A triangle inequality for p-resistance - 326:50
A triangle inequality for p-resistance - 426:57
A triangle inequality for p-resistance - 527:22
A triangle inequality for p-resistance - 629:14
A triangle inequality for p-resistance - 729:47
A triangle inequality for p-resistance - 830:18
Efficient prediction for tree markov random fields - 131:18
Efficient prediction for tree markov random fields - 233:40
Efficient prediction for tree markov random fields - 334:58
UCL Activities (Part II)36:13
Data dependent kernels in nearly-linear time - 137:37
Data dependent kernels in nearly-linear time - 238:11
Data dependent kernels in nearly-linear time - 340:09
Tighter PAC-Bayes bounds through distribution dependent priors - 142:40
Tighter PAC-Bayes bounds through distribution dependent priors - 244:18
Future directions44:46