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» On Extended Regular Expressions
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TSMC
2008
99views more  TSMC 2008»
14 years 10 months ago
Robust Regularized Kernel Regression
Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual fo...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
PAKDD
2005
ACM
184views Data Mining» more  PAKDD 2005»
15 years 3 months ago
Adjusting Mixture Weights of Gaussian Mixture Model via Regularized Probabilistic Latent Semantic Analysis
Mixture models, such as Gaussian Mixture Model, have been widely used in many applications for modeling data. Gaussian mixture model (GMM) assumes that data points are generated fr...
Luo Si, Rong Jin
PAMI
2011
14 years 5 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
KDD
2004
ACM
142views Data Mining» more  KDD 2004»
15 years 10 months ago
Meta-classification of Multi-type Cancer Gene Expression Data
Massive publicly available gene expression data consisting of different experimental conditions and microarray platforms introduce new challenges in data mining when integrating m...
Benny Y. M. Fung, Vincent T. Y. Ng
ICML
2005
IEEE
15 years 10 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...