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IJCAI
2007
15 years 7 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 6 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 11 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
JMLR
2002
90views more  JMLR 2002»
15 years 6 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 8 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