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ICDM
2003
IEEE
220views Data Mining» more  ICDM 2003»
10 years 8 days ago
Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining
Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled...
Kang Peng, Slobodan Vucetic, Bo Han, Hongbo Xie, Z...
ECCV
2010
Springer
10 years 10 days ago
Robust Multi-View Boosting with Priors
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
IJCNLP
2004
Springer
10 years 11 days ago
Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationships
Multi-document discourse analysis has emerged with the potential of improving various NLP applications. Based on the newly proposed Cross-document Structure Theory (CST), this pap...
Zhu Zhang, Dragomir R. Radev
DAGM
2004
Springer
10 years 12 days ago
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
Dengyong Zhou, Bernhard Schölkopf
COLT
2005
Springer
10 years 16 days ago
Generalization Error Bounds Using Unlabeled Data
We present two new methods for obtaining generalization error bounds in a semi-supervised setting. Both methods are based on approximating the disagreement probability of pairs of ...
Matti Kääriäinen
COLT
2005
Springer
10 years 16 days ago
A PAC-Style Model for Learning from Labeled and Unlabeled Data
Abstract. There has been growing interest in practice in using unlabeled data together with labeled data in machine learning, and a number of different approaches have been develo...
Maria-Florina Balcan, Avrim Blum
ICMCS
2005
IEEE
90views Multimedia» more  ICMCS 2005»
10 years 18 days ago
Integrating co-training and recognition for text detection
Training a good text detector requires a large amount of labeled data, which can be very expensive to obtain. Cotraining has been shown to be a powerful semi-supervised learning t...
Wen Wu, Datong Chen, Jie Yang
ICMCS
2005
IEEE
92views Multimedia» more  ICMCS 2005»
10 years 18 days ago
Semi-supervised meeting event recognition with adapted HMMs
This paper investigates the use of unlabeled data to help labeled data for audio-visual event recognition in meetings. To deal with situations in which it is difficult to collect...
Dong Zhang, Daniel Gatica-Perez, Samy Bengio
ICDM
2005
IEEE
138views Data Mining» more  ICDM 2005»
10 years 18 days ago
Labeling Unclustered Categorical Data into Clusters Based on the Important Attribute Values
Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points which are not sampled will not have t...
Hung-Leng Chen, Kun-Ta Chuang, Ming-Syan Chen
ICDM
2005
IEEE
185views Data Mining» more  ICDM 2005»
10 years 18 days ago
Semi-Supervised Mixture of Kernels via LPBoost Methods
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based o...
Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao
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