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COLING
2010
14 years 9 months ago
A Comparison of Models for Cost-Sensitive Active Learning
Active Learning (AL) is a selective sampling strategy which has been shown to be particularly cost-efficient by drastically reducing the amount of training data to be manually ann...
Katrin Tomanek, Udo Hahn
185
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KDD
2012
ACM
238views Data Mining» more  KDD 2012»
13 years 4 months ago
Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data
Incomplete data present serious problems when integrating largescale brain imaging data sets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiativ...
Lei Yuan, Yalin Wang, Paul M. Thompson, Vaibhav A....
CVPR
2012
IEEE
13 years 4 months ago
We are not contortionists: Coupled adaptive learning for head and body orientation estimation in surveillance video
In this paper, we deal with the estimation of body and head poses (i.e orientations) in surveillance videos, and we make three main contributions. First, we address this issue as ...
Cheng Chen, Jean-Marc Odobez
114
Voted
LWA
2007
15 years 3 months ago
Parameter Learning for a Readability Checking Tool
This paper describes the application of machine learning methods to determine parameters for DeLite, a readability checking tool. DeLite pinpoints text segments that are difficul...
Tim vor der Brück, Johannes Leveling
ECCV
2010
Springer
15 years 2 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