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Fast Support Vector Machines for Structural Kernels
Published on Oct 03, 20113109 Views
In this paper, we propose three important enhancements of the approximate cutting plane algorithm (CPA) to train Support Vector Machines with structural kernels: (i) we exploit a compact yet exact r
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Chapter list
Fast Support Vector Machines for Structural Kernels00:00
Structured Data00:19
Ex: Predicate-Argument Identification (1)01:20
Ex: Predicate-Argument Identification (2)01:46
Explicit feature vector representation01:52
State of the art in many tasks02:18
Training SVMs with Structural Kernels02:42
Key ideas in the paper03:01
CPA in a nutshell (1)03:49
CPA in a nutshell (2)04:22
CPA in a nutshell (3)04:59
CPA in a nutshell (4)05:09
CPA in a nutshell (5)05:14
Computing most violated constraint (MVC) (1)05:19
Computing most violated constraint (MVC) (2)06:00
Computing most violated constraint (MVC) (3)06:16
Computational bottleneck (1)06:41
Computational bottleneck (2)06:44
Approximate CPA + Structural Kernels07:14
Compact model representation using DAGs07:49
Three syntactic trees and the resulting DAG (1)08:36
Three syntactic trees and the resulting DAG (2)08:56
Three syntactic trees and the resulting DAG (3)09:04
Three syntactic trees and the resulting DAG (4)09:08
Computational bottleneck (3)09:20
SDAG (1)09:51
SDAG (2)09:57
SDAG+ (1)10:16
SDAG+ (2)10:32
Example: computing Kdag (1)10:45
Example: computing Kdag (2)10:55
Example: computing Kdag (3)11:15
Experimental setup11:28
Semantic Role Labeling (SRL) dataset12:03
Speedups during training on SRL dataset (100k)12:34
Classification speedups on SRL dataset (100k)13:46
Parallelization14:11
Speedups due to parallelization on 50k YA dataset14:44
Handling class-‐imbalance problem15:06
Results on QA classification: TREC and Yahoo! Answers16:08
Conclusions16:33
Future work17:26
Thank you!18:19