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NIPS
2004
9 years 8 months ago
Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms
In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We exp...
Omid Madani, David M. Pennock, Gary William Flake
NIPS
2004
9 years 8 months ago
Semi-supervised Learning by Entropy Minimization
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...
Yves Grandvalet, Yoshua Bengio
CASCON
2001
148views Education» more  CASCON 2001»
9 years 8 months ago
Email classification with co-training
The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algo...
Svetlana Kiritchenko, Stan Matwin
FLAIRS
2004
9 years 8 months ago
Semi-Supervised Sequence Classification with HMMs
Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervi...
Shi Zhong
EACL
2006
ACL Anthology
9 years 8 months ago
Bootstrapping Named Entity Recognition with Automatically Generated Gazetteer Lists
Current Named Entity Recognition systems suffer from the lack of hand-tagged data as well as degradation when moving to other domain. This paper explores two aspects: the automati...
Zornitsa Kozareva
ACL
2006
9 years 8 months ago
Boosting Statistical Word Alignment Using Labeled and Unlabeled Data
This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach ...
Hua Wu, Haifeng Wang, Zhan-yi Liu
AAAI
2006
9 years 8 months ago
Semi-supervised Multi-label Learning by Constrained Non-negative Matrix Factorization
We present a novel framework for multi-label learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key a...
Yi Liu, Rong Jin, Liu Yang
SDM
2007
SIAM
137views Data Mining» more  SDM 2007»
9 years 8 months ago
Semi-supervised Feature Selection via Spectral Analysis
Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called “small labeled-sample problem” in which labeled data is...
Zheng Zhao, Huan Liu
NIPS
2007
9 years 8 months ago
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
Ruslan Salakhutdinov, Geoffrey E. Hinton
NIPS
2007
9 years 8 months ago
Statistical Analysis of Semi-Supervised Regression
Semi-supervised methods use unlabeled data in addition to labeled data to construct predictors. While existing semi-supervised methods have shown some promising empirical performa...
John D. Lafferty, Larry A. Wasserman
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