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» Extensions to the Method of Multiplicities, with Application...
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PAMI
2006
206views more  PAMI 2006»
13 years 5 months ago
MILES: Multiple-Instance Learning via Embedded Instance Selection
Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called...
Yixin Chen, Jinbo Bi, James Ze Wang
CORR
2010
Springer
183views Education» more  CORR 2010»
13 years 3 months ago
Discovering shared and individual latent structure in multiple time series
This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared fe...
Suchi Saria, Daphne Koller, Anna Penn
ICASSP
2011
IEEE
12 years 9 months ago
Sparse common spatial patterns in brain computer interface applications
The Common Spatial Pattern (CSP) method is a powerful technique for feature extraction from multichannel neural activity and widely used in brain computer interface (BCI) applicat...
Fikri Goksu, Nuri Firat Ince, Ahmed H. Tewfik
LREC
2010
154views Education» more  LREC 2010»
13 years 6 months ago
CCASH: A Web Application Framework for Efficient, Distributed Language Resource Development
We introduce CCASH (Cost-Conscious Annotation Supervised by Humans), an extensible web application framework for cost-efficient annotation. CCASH provides a framework in which cos...
Paul Felt, Owen Merkling, Marc Carmen, Eric K. Rin...
ECCV
2010
Springer
13 years 5 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