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Kernel-based learning of hierarchial multilabel classification models
Published on Feb 25, 20073286 Views
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Chapter list
Kernel-based learning of hierarchial00:01
Hierarchical Multilabel Classification:00:53
How to learn hierarchical multilabels?02:12
How to measure loss?02:36
Scaling the loss06:22
The classification model09:18
Feature vectors11:19
From Maximum Likelihood to Maximum Margin13:43
Scaling the margin15:55
Primal optimization problem17:41
Dual problem18:51
Solving the optimization problem19:53
Marginalizing the problem21:04
Ensuring marginal consistency22:50
Marginalized problem23:55
Decomposing the problem24:59
Marginalized problem25:10
Decomposing the problem25:28
The optimization algorithm26:46
Conditional Gradient Ascent28:01
Conditional Gradient Ascent30:12
Conditional Gradient Ascent30:20
Conditional Gradient Ascent30:30
Conditional Gradient Ascent30:43
Conditional Gradient Ascent30:51
Conditional Gradient Ascent30:55
Conditional Gradient Ascent31:13
Working set maintenance31:27
Experiments32:54
Example learning curve34:44
Results36:27
Conclusions38:18