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» Stability of Feature Selection Algorithms
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SCALESPACE
2005
Springer
15 years 8 months ago
Discrete Representation of Top Points via Scale Space Tessellation
In previous work, singular points (or top points) in the scale space representation of generic images have proven valuable for image matching. In this paper, we propose a construct...
Bram Platel, M. Fatih Demirci, Ali Shokoufandeh, L...
MM
2005
ACM
171views Multimedia» more  MM 2005»
15 years 8 months ago
Semantic manifold learning for image retrieval
Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...
Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
CVPR
2010
IEEE
15 years 11 months ago
Online Multiple Instance Learning with No Regret
Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been u...
Li Mu, James Kwok, Lu Bao-liang
CORR
2010
Springer
174views Education» more  CORR 2010»
15 years 3 months ago
Gaussian Process Bandits for Tree Search
We motivate and analyse a new Tree Search algorithm, based on recent advances in the use of Gaussian Processes for bandit problems. We assume that the function to maximise on the ...
Louis Dorard, John Shawe-Taylor
167
Voted
FCSC
2007
159views more  FCSC 2007»
15 years 3 months ago
Ranking with uncertain labels and its applications
1 The techniques for image analysis and classi cation generally consider the image sample labels xed and without uncertainties. The rank regression problem is studied in this pape...
Shuicheng Yan, Huan Wang, Jianzhuang Liu, Xiaoou T...