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SIGIR
2008
ACM
15 years 6 months ago
Learning from labeled features using generalized expectation criteria
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
Gregory Druck, Gideon S. Mann, Andrew McCallum
PAMI
2011
15 years 1 months ago
Greedy Learning of Binary Latent Trees
—Inferring latent structures from observations helps to model and possibly also understand underlying data generating processes. A rich class of latent structures are the latent ...
Stefan Harmeling, Christopher K. I. Williams
ICPR
2002
IEEE
16 years 7 months ago
Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning
A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavatio...
David B. Cooper, Andrew R. Willis, Stuart Andrews,...
179
Voted
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
16 years 7 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen
189
Voted
IJCNN
2000
IEEE
15 years 11 months ago
Regression Analysis for Rival Penalized Competitive Learning Binary Tree
The main aim of this paper is to develop a suitable regression analysis model for describing the relationship between the index efficiency and the parameters of the Rival Penaliz...
Xuequn Li, Irwin King