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Learning from Labeled and Unlabelled Data: When the Smoothness Assumption Holds
Published on Mar 11, 20114790 Views
During recent years, there has been a growing interest in learning algorithms capable of utilizing both labeled and unlabeled data for prediction tasks. The reason for this attention is the cost of
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Chapter list
Learning from Labeled and Unlabelled Data: When the Smoothness Assumption Holds00:00
Transductive/semi-supervised vs Inductive Learning in predictive tasks (1)02:54
Transductive/semi-supervised vs Inductive Learning in predictive tasks (2)03:42
Motivation for Transductive/Semi- Supervised Learning (1)05:10
Motivation for Transductive/Semi- Supervised Learning (2)05:18
Transductive vs Semi-supervised learning: differences (1)06:29
Transductive vs Semi-supervised learning: differences (2)07:39
Transductive vs Semi-supervised learning: differences (3)08:26
Transductive learning vs. inductive learning vs. semi-supervised learning09:32
Transductive learning: early references10:11
Smoothness assumption in supervised learning11:30
Semi-supervised smoothness assumption (1)13:01
Semi-supervised smoothness assumption (2)13:45
Semi-supervised smoothness assumption (3)14:47
Cluster assumption15:28
Transductive learning: which assumptions16:15
Transductive learning vs. local learning16:34
Autocorrelation17:44
Positive Autocorrelation19:20
Positive autocorrelation and the smootness assumption (1)20:03
Positive autocorrelation and the smootness assumption (2)21:14
Spatial Data22:37
Networked Data23:27
Opportunities for Transductive learning23:58
HSGT: Hierarchical classification of textual documents24:56
HSGT in WebClass: the problem25:00
HSGT in WebClass: basic idea26:09
HSGT in WebClass: the problem (1)27:48
Document representation29:35
HSGT in WebClass: the problem (2)30:23
HSGT in WebClass: the problem (3)30:55
Relevant Example Selection31:40
Experiments32:37
Some Experimental Results33:50
SPRECO (SPatial REgression with CO-training)34:26
Transductive Spatial Regression (1)34:36
Transductive Spatial Regression (2)35:27
The solution: SPatial REgression with CO-training (1)35:47
The solution: SPatial REgression with CO-training (2)36:09
Two Views of Spatial Data37:19
Determine reliability of labels (1)38:07
Determine reliability of labels (2)39:55
Labeling40:40
Experiments (1)42:05
Experiments (2)42:34
TRANSC (TRANsductive Structural Classifier) (1)43:01
TRANSC (TRANsductive Structural Classifier) (2)43:05
TRANSC: problem definition46:55
TRANSC: algorithm48:34
TRANSC (1)50:05
TRANSC (2)50:18
TRANSC (3)50:57
North-West England Census Data (1)51:06
North-West England Census Data (2)52:13
Munich Census Data (1)52:49
Munich Census Data (2)53:32
Thanks53:51