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» Learning Generative Models with the Up-Propagation Algorithm
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ICML
2010
IEEE
13 years 8 months ago
Non-Local Contrastive Objectives
Pseudo-likelihood and contrastive divergence are two well-known examples of contrastive methods. These algorithms trade off the probability of the correct label with the probabili...
David Vickrey, Cliff Chiung-Yu Lin, Daphne Koller
KDD
2004
ACM
181views Data Mining» more  KDD 2004»
14 years 8 months ago
Column-generation boosting methods for mixture of kernels
We devise a boosting approach to classification and regression based on column generation using a mixture of kernels. Traditional kernel methods construct models based on a single...
Jinbo Bi, Tong Zhang, Kristin P. Bennett
AIRS
2009
Springer
14 years 2 months ago
A Latent Dirichlet Framework for Relevance Modeling
Relevance-based language models operate by estimating the probabilities of observing words in documents relevant (or pseudo relevant) to a topic. However, these models assume that ...
Viet Ha-Thuc, Padmini Srinivasan
SIGMOD
2009
ACM
175views Database» more  SIGMOD 2009»
14 years 8 months ago
Keyword search on structured and semi-structured data
Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex a...
Yi Chen, Wei Wang 0011, Ziyang Liu, Xuemin Lin
SIGIR
2005
ACM
14 years 1 months ago
Linear discriminant model for information retrieval
This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate a...
Jianfeng Gao, Haoliang Qi, Xinsong Xia, Jian-Yun N...