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First ACM International Conference on Web Search and Data Mining - WSDM 2008

Fast learning of Document Ranking Functions with the Committee Perceptron

author: Jonathan Elsas, Carnegie Mellon University

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*Slides*
0:00	Rank Learning with the Committee Perceptron (1)
0:08	Rank Learning with the Committee Perceptron (2)
0:12	A Brief History of Features in IR (1)
0:17	A Brief History of Features in IR (2)
0:31	A Brief History of Features in IR (3)
0:39	A Brief History of Features in IR (4)
0:48	A Brief History of Features in IR (5)
0:56	Example Features (1)
1:00	Example Features (2)
1:06	Example Features (3)
1:09	Example Features (4)
1:12	Example Features (5)
1:25	How do we use all these features? (1)
1:30	How do we use all these features? (2)
1:36	How do we use all these features? (3)
1:48	How do we use all these features? (4)
1:56	Learning to Rank (LETOR)
2:12	Pairwise Preference Learning (1)
2:55	Pairwise Preference Learning (2)
3:11	Pairwise Preference Learning (3)
3:41	Pairwise Preference Learning (4)
3:52	Pairwise Preference Learning: Linear Setting
4:26	Perceptron Algorithm (1)
5:10	Perceptron Algorithm (2)
5:46	Perceptron Algorithm (3)
6:22	Perceptron Algorithm (4)
6:25	Perceptron Algorithm (5)
6:32	Perceptron Algorithm (6)
6:40	Perceptron Algorithm (7)
6:43	Perceptron Algorithm (8)
6:47	Perceptron Algorithm (9)
6:49	Perceptron Algorithm (10)
6:56	Perceptron Algorithm (11)
6:58	Perceptron Algorithm (12)
7:00	Perceptron Algorithm (13)
7:05	Perceptron Algorithm (14)
7:09	Perceptron Algorithm (15)
7:14	Perceptron Algorithm (16)
7:31	Perceptron Algorithm (17)
7:33	Perceptron Algorithm (18)
7:35	Perceptron Algorithm (19)
7:44	Perceptron Algorithm (20)
7:54	Perceptron Algorithm (21)
8:07	Perceptron Algorithm (22)
8:24	Perceptron Algorithm (23)
8:26	Perceptron Algorithm (24)
8:51	Perceptron Algorithm (25)
8:54	Perceptron Algorithm (26)
9:08	Perceptron Algorithm (27)
9:10	Perceptron Algorithm (28)
9:21	Perceptron Algorithm (29)
9:38	Perceptron Algorithm (30)
10:01	Perceptron Algorithm (31)
10:19	Committee Perceptron (1)
10:30	Committee Perceptron (2)
10:38	Committee Perceptron (3)
10:41	Committee Perceptron (4)
10:46	Committee Perceptron (5)
11:05	Committee Perceptron (6)
11:24	Test Collection: LETOR Data set (1)
11:55	Test Collection: LETOR Data set (2)
12:33	MAP for Perceptron Variants on OHSUMED
13:16	Training Time (1)
13:20	Training Time (2)
13:32	Training Time (3)
13:36	Training Time (4)
13:42	NDCG@n for Committee Perceptron and baselines on OHSUMED
14:17	CP Performance: OHSUMED
14:33	CP Performance: TD2003
14:41	CP Performance: TD2004
14:49	Conclusions
15:22	Thank You!
16:47	- Questions
17:01	- Questions

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