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IJCAI
2007
15 years 3 months ago
Learning to Identify Unexpected Instances in the Test Set
Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instan...
Xiaoli Li, Bing Liu, See-Kiong Ng
BMCBI
2008
134views more  BMCBI 2008»
15 years 2 months ago
Identification of transcription factor contexts in literature using machine learning approaches
Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While man...
Hui Yang, Goran Nenadic, John A. Keane
CAIP
2003
Springer
222views Image Analysis» more  CAIP 2003»
15 years 7 months ago
Learning Statistical Structure for Object Detection
Abstract. Many classes of images exhibit sparse structuring of statistical dependency. Each variable has strong statistical dependency with a small number of other variables and ne...
Henry Schneiderman
104
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JMLR
2002
90views more  JMLR 2002»
15 years 1 months ago
Machine Learning with Data Dependent Hypothesis Classes
We extend the VC theory of statistical learning to data dependent spaces of classifiers. This theory can be viewed as a decomposition of classifier design into two components; the...
Adam Cannon, J. Mark Ettinger, Don R. Hush, Clint ...
ICCV
2007
IEEE
16 years 3 months ago
Boosting Invariance and Efficiency in Supervised Learning
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
Andrea Vedaldi, Paolo Favaro, Enrico Grisan