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» Generic Incremental Algorithms for Local Search
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CIKM
2010
Springer
13 years 8 months ago
Novel local features with hybrid sampling technique for image retrieval
In image retrieval, most existing approaches that incorporate local features produce high dimensional vectors, which lead to a high computational and data storage cost. Moreover, ...
Leszek Kaliciak, Dawei Song, Nirmalie Wiratunga, J...
ECML
2007
Springer
13 years 11 months ago
Sequence Labeling with Reinforcement Learning and Ranking Algorithms
Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assi...
Francis Maes, Ludovic Denoyer, Patrick Gallinari
CVPR
2006
IEEE
14 years 11 months ago
Training Deformable Models for Localization
We present a new method for training deformable models. Assume that we have training images where part locations have been labeled. Typically, one fits a model by maximizing the l...
Deva Ramanan, Cristian Sminchisescu
ICDM
2007
IEEE
169views Data Mining» more  ICDM 2007»
14 years 1 months ago
Efficient Discovery of Frequent Approximate Sequential Patterns
We propose an efficient algorithm for mining frequent approximate sequential patterns under the Hamming distance model. Our algorithm gains its efficiency by adopting a "brea...
Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu
CP
2008
Springer
13 years 11 months ago
Exploiting Decomposition in Constraint Optimization Problems
Decomposition is a powerful technique for reducing the size of a backtracking search tree. However, when solving constraint optimization problems (COP's) the standard techniqu...
Matthew Kitching, Fahiem Bacchus