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COLING
2008
9 years 9 months ago
On Robustness and Domain Adaptation using SVD for Word Sense Disambiguation
In this paper we explore robustness and domain adaptation issues for Word Sense Disambiguation (WSD) using Singular Value Decomposition (SVD) and unlabeled data. We focus on the s...
Eneko Agirre, Oier Lopez de Lacalle
AAAI
2010
9 years 9 months ago
Cost-Sensitive Semi-Supervised Support Vector Machine
In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequa...
Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou
ACL
2008
9 years 9 months ago
Semi-Supervised Sequential Labeling and Segmentation Using Giga-Word Scale Unlabeled Data
This paper provides evidence that the use of more unlabeled data in semi-supervised learning can improve the performance of Natural Language Processing (NLP) tasks, such as part-o...
Jun Suzuki, Hideki Isozaki
COLT
2008
Springer
9 years 9 months ago
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
Shai Ben-David, Tyler Lu, Dávid Pál
COLT
2008
Springer
9 years 9 months ago
An Information Theoretic Framework for Multi-view Learning
In the multi-view learning paradigm, the input variable is partitioned into two different views X1 and X2 and there is a target variable Y of interest. The underlying assumption i...
Karthik Sridharan, Sham M. Kakade
BIBM
2008
IEEE
125views Bioinformatics» more  BIBM 2008»
9 years 9 months ago
On the Role of Local Matching for Efficient Semi-supervised Protein Sequence Classification
Recent studies in protein sequence analysis have leveraged the power of unlabeled data. For example, the profile and mismatch neighborhood kernels have shown significant improveme...
Pavel P. Kuksa, Pai-Hsi Huang, Vladimir Pavlovic
DASFAA
2004
IEEE
135views Database» more  DASFAA 2004»
9 years 11 months ago
Semi-supervised Text Classification Using Partitioned EM
Text classification using a small labeled set and a large unlabeled data is seen as a promising technique to reduce the labor-intensive and time consuming effort of labeling traini...
Gao Cong, Wee Sun Lee, Haoran Wu, Bing Liu
AMT
2006
Springer
147views Multimedia» more  AMT 2006»
9 years 11 months ago
Semi-Supervised Text Classification Using Positive and Unlabeled Data
Text classification using positive and unlabeled data refers to the problem of building text classifier using positive documents (P) of one class and unlabeled documents (U) of man...
Shuang Yu, Xueyuan Zhou, Chunping Li
PERCOM
2010
ACM
9 years 11 months ago
Indoor localization in multi-floor environments with reduced effort
Abstract—In pervasive computing, localizing a user in wireless indoor environments is an important yet challenging task. Among the state-of-art localization methods, fingerprint...
Hua-Yan Wang, Vincent Wenchen Zheng, Junhui Zhao, ...
ICDM
2003
IEEE
210views Data Mining» more  ICDM 2003»
10 years 1 months ago
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
Hua-Jun Zeng, Xuanhui Wang, Zheng Chen, Hongjun Lu...
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