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» A theory of learning with similarity functions
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JMLR
2008
116views more  JMLR 2008»
15 years 1 months ago
Support Vector Machinery for Infinite Ensemble Learning
Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of some base hypotheses. Nevertheless, most existing algorithms are ...
Hsuan-Tien Lin, Ling Li
SIGCSE
2002
ACM
229views Education» more  SIGCSE 2002»
15 years 1 months ago
GraphicsMentor: a tool for learning graphics fundamentals
This paper discusses the functionality of GraphicsMentor. GraphicsMentor permits a student to modify many parameters of the camera, objects, and light sources interactively, and t...
Dejan Nikolic, Ching-Kuang Shene
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
16 years 2 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
ICML
2009
IEEE
16 years 2 months ago
Domain adaptation from multiple sources via auxiliary classifiers
We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for l...
Lixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua
108
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ICML
2004
IEEE
16 years 2 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...