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PAMI
2006
206views more  PAMI 2006»
15 years 2 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
ICDAR
2009
IEEE
15 years 7 hour ago
Document Content Extraction Using Automatically Discovered Features
We report an automatic feature discovery method that achieves results comparable to a manually chosen, larger feature set on a document image content extraction problem: the locat...
Sui-Yu Wang, Henry S. Baird, Chang An
ICPR
2004
IEEE
16 years 3 months ago
Robust Feature Matching Across Widely Separated Color Images
We present a novel method for feature matching across widely separated color images. The proposed approach is robust and can support various correspondence based algorithms e.g. t...
Alexander Kaplan, Ehud Rivlin, Ilan Shimshoni
130
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ICML
2007
IEEE
16 years 3 months ago
Learning a meta-level prior for feature relevance from multiple related tasks
In many prediction tasks, selecting relevant features is essential for achieving good generalization performance. Most feature selection algorithms consider all features to be a p...
Su-In Lee, Vassil Chatalbashev, David Vickrey, Dap...
CORR
2010
Springer
123views Education» more  CORR 2010»
15 years 25 days ago
Feature Construction for Relational Sequence Learning
Abstract. We tackle the problem of multi-class relational sequence learning using relevant patterns discovered from a set of labelled sequences. To deal with this problem, firstly...
Nicola Di Mauro, Teresa Maria Altomare Basile, Ste...