Semi-supervised Structured Prediction Models

author: Ulf Brefeld, Department of Computer Science, Humboldt-Universität zu Berlin

Categories

Top: Computer Science: Machine Learning: Structured Output
Top: Computer Science: Machine Learning: Semi-supervised Learning

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*Slides*
0:00	Semi-supervised Structured Prediction Models
0:28	Binary Classification
1:03	Label Sequence Learning
1:29	Natural Language Parsing
1:39	Structural Learning
2:35	Semi-supervised Discriminative Learning
3:21	Overview-part01
3:53	Overview-part02
3:58	Cluster Assumption
4:50	Learning from Multiple Views / Co-learning
5:29	Hypothesis Space Intersection
6:28	Co-optimization Problem
7:05	Overview-part03
7:13	Semi-supervised Regularized Least Squares Regression
7:50	Co-regularized Least Squares Regression-part01
8:24	Co-regularized Least Squares Regression-part02
8:56	Semi-parametric Approximation-part01
9:27	Semi-parametric Approximation-part02
9:59	Semi-supervised Methods for Distributed Data
10:43	Empirical Results-part01
11:57	Empirical Results-part02
12:18	Execution Time-part01
12:29	Overview-part04
12:31	Semi-supervised Learning for Structured Output Variables
13:47	CoSVM Optimization Problem
16:18	Labeled Examples, View v=1,2
17:46	Unlabeled Examples
18:41	Biocreative Named Entity Recognition
19:09	Biocreative Gene/Protein Name Recognition
20:01	Natural Language Parsing
20:21	Wall Street Journal / Negra Corpus Natural Language Parsing
21:08	Execution Time-part02
22:00	Overview-part05
25:56	Transductive Support Vector Machines for Structured Variables
26:49	Unconstraint Support Vector Machines-part01
27:19	Unconstraint Support Vector Machines-part02
28:18	Unconstraint Transductive Support Vector Machines
29:34	Execution Time-part03
30:26	Spanish News Wire Named Entity Recognition
30:41	Spanish News Named Entity Recognition
31:13	Artificial Sequential Data
34:14	Overview-part06
34:31	Supervised Clustering of Data Streams for Email Batch Detection
35:54	Template Generated Spam Messages
36:33	Correlation Clustering
37:26	Problem Setting
38:07	Large Margin Approach
38:51	Exploit Data Stream!
39:11	Sequential Approximation
39:29	Results for Batch Detection
39:47	Execution Time-part04
40:04	Supervised Clustering of Data Streams for Email Batch Detection (P. Haider, U. Brefeld und T. Scheffer, ICML 2007)
41:07	Overview-part07
41:12	Conclusion-part01

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