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JMLR
2008
104views more  JMLR 2008»
13 years 8 months ago
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new cl...
Giorgio Corani, Marco Zaffalon
ICDM
2005
IEEE
162views Data Mining» more  ICDM 2005»
14 years 2 months ago
Mining Patterns That Respond to Actions
Data mining focuses on patterns that summarize the data. In this paper, we focus on mining patterns that could change the state by responding to opportunities of actions.
Yuelong Jiang, Ke Wang, Alexander Tuzhilin, Ada Wa...
JMLR
2010
144views more  JMLR 2010»
13 years 3 months ago
Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables
In this paper we address the problem of predicting when the available data is incomplete. We show that changing the generally accepted table-wise view of the sample items into a g...
Sándor Szedmák, Yizhao Ni, Steve R. ...
ICCV
2007
IEEE
14 years 3 months ago
Surface-from-Gradients with Incomplete Data for Single View Modeling
Surface gradients are useful to surface reconstruction in single view modeling, shape-from-shading, and photometric stereo. Previous algorithms minimize a complex, nonlinear energ...
Heung-Sun Ng, Tai-Pang Wu, Chi-Keung Tang
MA
2010
Springer
98views Communications» more  MA 2010»
13 years 7 months ago
The Stein phenomenon for monotone incomplete multivariate normal data
We establish the Stein phenomenon in the context of two-step, monotone incomplete data drawn from Np+q(µ, Σ), a multivariate normal population with mean µ and covariance matrix...
Donald St. P. Richards, Tomoya Yamada