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» Learning to learn with the informative vector machine
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PAMI
2006
147views more  PAMI 2006»
15 years 4 months ago
Bayesian Gaussian Process Classification with the EM-EP Algorithm
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
Hyun-Chul Kim, Zoubin Ghahramani
SMC
2010
IEEE
132views Control Systems» more  SMC 2010»
15 years 2 months ago
Selection of SIFT feature points for scene description in robot vision
This paper presents a method for selection of SIFT(Scale-Invariant Feature Transform) feature points using OC-SVM (One Class-Support Vector Machines). We proposed the method for au...
Yuya Utsumi, Masahiro Tsukada, Hirokazu Madokoro, ...
CIKM
2008
Springer
15 years 6 months ago
Trada: tree based ranking function adaptation
Machine Learned Ranking approaches have shown successes in web search engines. With the increasing demands on developing effective ranking functions for different search domains, ...
Keke Chen, Rongqing Lu, C. K. Wong, Gordon Sun, La...
WEBI
2005
Springer
15 years 10 months ago
Integrating Element and Term Semantics for Similarity-Based XML Document Clustering
Structured link vector model (SLVM) is a recently proposed document representation that takes into account both structural and semantic information for measuring XML document simi...
Jianwu Yang, William K. Cheung, Xiaoou Chen
IDA
1998
Springer
15 years 4 months ago
Self-Organized-Expert Modular Network for Classification of Spatiotemporal Sequences
We investigate a form of modular neural network for classification with (a) pre-separated input vectors entering its specialist (expert) networks, (b) specialist networks which ar...
Sylvian R. Ray, William H. Hsu