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» Learning the Structure of Linear Latent Variable Models
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FCSC
2010
238views more  FCSC 2010»
13 years 6 months ago
Knowledge discovery through directed probabilistic topic models: a survey
Graphical models have become the basic framework for topic based probabilistic modeling. Especially models with latent variables have proved to be effective in capturing hidden str...
Ali Daud, Juanzi Li, Lizhu Zhou, Faqir Muhammad
JMLR
2010
192views more  JMLR 2010»
13 years 3 months ago
Efficient Learning of Deep Boltzmann Machines
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
Ruslan Salakhutdinov, Hugo Larochelle
ESWA
2008
204views more  ESWA 2008»
13 years 9 months ago
A comparison of neural network and multiple regression analysis in modeling capital structure
Empirical studies of the variation in debt ratios across firms have used statistical models singularly to analyze the important determinants of capital structure. Researchers, how...
Hsiao-Tien Pao
NAACL
2010
13 years 6 months ago
Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions
We describe a method of incorporating taskspecific cost functions into standard conditional log-likelihood (CLL) training of linear structured prediction models. Recently introduc...
Kevin Gimpel, Noah A. Smith
ICDM
2010
IEEE
197views Data Mining» more  ICDM 2010»
13 years 6 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...