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» Learning Object Representations Using Sequential Patterns
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ECML
2006
Springer
13 years 11 months ago
Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data
Multiple-instance learning (MIL) is a popular concept among the AI community to support supervised learning applications in situations where only incomplete knowledge is available....
Corneliu Henegar, Karine Clément, Jean-Dani...
FLAIRS
2009
13 years 5 months ago
Mapping Grounded Object Properties across Perceptually Heterogeneous Embodiments
As robots become more common, it becomes increasingly useful for them to communicate and effectively share knowledge that they have learned through their individual experiences. L...
Zsolt Kira
KBS
1998
122views more  KBS 1998»
13 years 7 months ago
Concept formation in design
: This paper presents a computationally tractable view on where simple design concepts come from by proposing a paradigm for the formation of design concepts based on the emergence...
John S. Gero
WACV
2012
IEEE
12 years 3 months ago
Online discriminative object tracking with local sparse representation
We propose an online algorithm based on local sparse representation for robust object tracking. Local image patches of a target object are represented by their sparse codes with a...
Qing Wang, Feng Chen, Wenli Xu, Ming-Hsuan Yang
KDD
2002
ACM
125views Data Mining» more  KDD 2002»
14 years 8 months ago
Pattern discovery in sequences under a Markov assumption
In this paper we investigate the general problem of discovering recurrent patterns that are embedded in categorical sequences. An important real-world problem of this nature is mo...
Darya Chudova, Padhraic Smyth