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» Generative models for similarity-based classification
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EMNLP
2006
13 years 6 months ago
Competitive generative models with structure learning for NLP classification tasks
In this paper we show that generative models are competitive with and sometimes superior to discriminative models, when both kinds of models are allowed to learn structures that a...
Kristina Toutanova
ICDM
2010
IEEE
197views Data Mining» more  ICDM 2010»
13 years 3 months ago
D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification
: D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi HP Labo...
Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yu...
PR
2008
104views more  PR 2008»
13 years 5 months ago
Generative models for similarity-based classification
A maximum-entropy approach to generative similarity-based classifiers model is proposed. First, a descriptive set of similarity statistics is assumed to be sufficient for classifi...
Luca Cazzanti, Maya R. Gupta, Anjali J. Koppal
ICDM
2008
IEEE
104views Data Mining» more  ICDM 2008»
13 years 12 months ago
A Generative Probabilistic Model for Multi-label Classification
Hongning Wang, Minlie Huang, Xiaoyan Zhu
ICPR
2006
IEEE
14 years 6 months ago
Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning
It is possible to broadly characterize two approaches to probabilistic modeling in terms of generative and discriminative methods. Provided with sufficient training data the discr...
B. Michael Kelm, Chris Pal, Andrew McCallum