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ECCV
2010
Springer
15 years 3 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
BMCBI
2006
198views more  BMCBI 2006»
15 years 3 months ago
Gene selection and classification of microarray data using random forest
Background: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of ...
Ramón Díaz-Uriarte, Sara Alvarez de ...
JMLR
2010
166views more  JMLR 2010»
14 years 10 months ago
Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models
We present new measures of the causal direction between two non-gaussian random variables. They are based on the likelihood ratio under the linear non-gaussian acyclic model (LiNG...
Aapo Hyvärinen
ICCV
2007
IEEE
16 years 5 months ago
Population Shape Regression From Random Design Data
Regression analysis is a powerful tool for the study of changes in a dependent variable as a function of an independent regressor variable, and in particular it is applicable to t...
Bradley C. Davis, P. Thomas Fletcher, Elizabeth Bu...
APJOR
2010
118views more  APJOR 2010»
15 years 1 months ago
Alternative Randomization for Valuing American Options
This paper provides a fast and accurate randomization algorithm for valuing American puts and calls on dividend-paying stocks and their early exercise boundaries. The primal focus...
Toshikazu Kimura