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PAMI
2011
14 years 11 months ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
CVPR
2007
IEEE
16 years 6 months ago
Multiple Instance Learning of Pulmonary Embolism Detection with Geodesic Distance along Vascular Structure
We propose a novel classification approach for automatically detecting pulmonary embolism (PE) from computedtomography-angiography images. Unlike most existing approaches that req...
Jinbo Bi, Jianming Liang
GECCO
2004
Springer
137views Optimization» more  GECCO 2004»
15 years 9 months ago
Evolving Better Multiple Sequence Alignments
Aligning multiple DNA or protein sequences is a fundamental step in the analyses of phylogeny, homology and molecular structure. Heuristic algorithms are applied because optimal mu...
Luke Sheneman, James A. Foster
139
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ICCV
2009
IEEE
16 years 9 months ago
Robust Fitting of Multiple Structures: The Statistical Learning Approach
We propose an unconventional but highly effective approach to robust fitting of multiple structures by using statistical learning concepts. We design a novel Mercer kernel for t...
Tat-Jun Chin, Hanzi Wang, David Suter
IJCNN
2000
IEEE
15 years 8 months ago
Predictive Multiple Model Switching Control with the Self-Organizing Map
—A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framewor...
Mark A. Motter