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JMLR
2010
155views more  JMLR 2010»
13 years 6 months ago
Approximate Tree Kernels
Convolution kernels for trees provide simple means for learning with tree-structured data. The computation time of tree kernels is quadratic in the size of the trees, since all pa...
Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Ro...
MICCAI
2010
Springer
13 years 6 months ago
Understanding the Optics to Aid Microscopy Image Segmentation
Abstract. Image segmentation is essential for many automated microscopy image analysis systems. Rather than treating microscopy images as general natural images and rushing into th...
Zhaozheng Yin, Kang Li, Takeo Kanade, Mei Chen
PAMI
2010
337views more  PAMI 2010»
13 years 6 months ago
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
—This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based ...
Kwang In Kim, Younghee Kwon
PRL
2010
105views more  PRL 2010»
13 years 6 months ago
Quasi-random nonlinear scale space
A novel nonlinear scale space framework is proposed for the purpose of multiscale image representation. The scale space decomposition problem is formulated as a general Bayesian l...
Akshaya Kumar Mishra, Alexander Wong, David A. Cla...
SDM
2010
SIAM
168views Data Mining» more  SDM 2010»
13 years 6 months ago
Convex Principal Feature Selection
A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the o...
Mahdokht Masaeli, Yan Yan, Ying Cui, Glenn Fung, J...