en-de
en-es
en-fr
en-pt
en-sl
en
en-zh
0.25
0.5
0.75
1.25
1.5
1.75
2
On the Consistency of Multi-Label Learning
Published on Aug 02, 20113333 Views
Multi-label learning has attracted much attention during the past few years. Many multilabel learning approaches have been developed, mostly working with surrogate loss functions since multi-label los
Related categories
Chapter list
On the Consistency of Multi-Label Learning00:00
Outline - 100:00
Multi-Label Learning00:06
Multi-Label Learning (cont.)00:18
Multi-label Learning Setup00:40
Surrogate Loss01:15
Binary Classification01:37
Problems01:59
Previous Consistent Work02:50
Outline - 203:24
Conditional Risk03:27
Consistency Theorem03:55
Ranking Loss04:33
Surrogate Loss05:20
Inconsistency for Ranking Loss05:44
What is the Problem?06:46
Partial Ranking Loss07:30
Consistency for Partial Ranking Loss08:20
Inconsistency for Partial Ranking Loss08:54
Hamming Loss09:26
Inconsistency for Deterministic Case10:13
Consistency for Deterministic Case10:41
Conclusion10:49
Future Work11:35