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ICML
2003
IEEE
16 years 5 months ago
Learning on the Test Data: Leveraging Unseen Features
This paper addresses the problem of classification in situations where the data distribution is not homogeneous: Data instances might come from different locations or times, and t...
Benjamin Taskar, Ming Fai Wong, Daphne Koller
ECCV
2010
Springer
15 years 4 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
MICCAI
2000
Springer
15 years 7 months ago
Small Sample Size Learning for Shape Analysis of Anatomical Structures
We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medic...
Polina Golland, W. Eric L. Grimson, Martha Elizabe...
BMCBI
2010
143views more  BMCBI 2010»
15 years 4 months ago
Learning gene regulatory networks from only positive and unlabeled data
Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled...
Luigi Cerulo, Charles Elkan, Michele Ceccarelli
SIGIR
2008
ACM
15 years 4 months ago
Learning query intent from regularized click graphs
This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a ...
Xiao Li, Ye-Yi Wang, Alex Acero