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COLT
2007
Springer
10 years 1 months ago
Multi-view Regression Via Canonical Correlation Analysis
In the multi-view regression problem, we have a regression problem where the input variable (which is a real vector) can be partitioned into two different views, where it is assum...
Sham M. Kakade, Dean P. Foster
IJCNN
2008
IEEE
10 years 1 months ago
Learning adaptive subject-independent P300 models for EEG-based brain-computer interfaces
Abstract— This paper proposes an approach to learn subjectindependent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of exis...
Shijian Lu, Cuntai Guan, Haihong Zhang
ICDM
2008
IEEE
97views Data Mining» more  ICDM 2008»
10 years 1 months ago
Semi-supervised Learning from General Unlabeled Data
We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better...
Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. L...
DASFAA
2008
IEEE
120views Database» more  DASFAA 2008»
10 years 1 months ago
Knowledge Transferring Via Implicit Link Analysis
In this paper, we design a local classification algorithm using implicit link analysis, considering the situation that the labeled and unlabeled data are drawn from two different ...
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Yong Yu
ICDAR
2009
IEEE
10 years 1 months ago
Evaluating Retraining Rules for Semi-Supervised Learning in Neural Network Based Cursive Word Recognition
Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, inc...
Volkmar Frinken, Horst Bunke
PAKDD
2009
ACM
151views Data Mining» more  PAKDD 2009»
10 years 1 months ago
Budget Semi-supervised Learning
In this paper we propose to study budget semi-supervised learning, i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/...
Zhi-Hua Zhou, Michael Ng, Qiao-Qiao She, Yuan Jian...
MCS
2009
Springer
10 years 1 months ago
When Semi-supervised Learning Meets Ensemble Learning
Abstract. Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; th...
Zhi-Hua Zhou
SDM
2009
SIAM
105views Data Mining» more  SDM 2009»
10 years 4 months ago
Exploiting Semantic Constraints for Estimating Supersenses with CRFs.
The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even hu...
Gerhard Paaß, Frank Reichartz
KDD
2002
ACM
157views Data Mining» more  KDD 2002»
10 years 7 months ago
Exploiting unlabeled data in ensemble methods
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...
Kristin P. Bennett, Ayhan Demiriz, Richard Maclin
KDD
2008
ACM
161views Data Mining» more  KDD 2008»
10 years 7 months ago
Spectral domain-transfer learning
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, ...
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