The 25th International Conference on Machine Learning (ICML 2008)

Hierarchical sampling for active learning

author: Daniel Hsu, UCSD

Description

We present an active learning scheme that exploits cluster structure in data.

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*Slides*
0:00	Hierarchical Sampling for Active Learning
0:06	Active Learning
1:01	General Active Learning Strategies - 1
2:20	General Active Learning Strategies - 2
2:24	Typical Active Learning Heuristics - 1
3:03	Typical Active Learning Heuristics - 2
3:13	Typical Active Learning Heuristics - 3
3:40	Typical Active Learning Heuristics - 4
3:56	Typical Active Learning Heuristics - 5
4:25	Typical Active Learning Heuristics - 6
4:53	Consistency with Active Learning
5:32	Cluster-Adaptive Sampling - 1
6:09	Cluster-Adaptive Sampling - 2
6:17	Cluster-Adaptive Sampling - 3
6:24	Cluster-Adaptive Sampling - 4
6:33	Cluster-Adaptive Sampling - 5
6:48	Cluster-Adaptive Sampling - 6
7:13	Cluster-Adaptive Sampling - 7
7:19	Cluster-Adaptive Sampling - 8
7:29	Cluster-Adaptive Sampling - 9
8:48	Algorithm - 1
9:49	Algorithm - 2
10:01	Algorithm Details - 1
10:32	Algorithm Details - 2
11:47	Algorithm Details - 3
13:03	Consistency Guarantees
14:05	Immediate Extensions
15:11	Experiments - 1
15:53	Experiments - 2
17:27	Future Work
18:17	Summary
18:54	- Questions

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