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» Optimized fixed-size kernel models for large data sets
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CVPR
2010
IEEE
14 years 4 months ago
Locally-Parametric Pictorial Structures
Pictorial structure (PS) models are extensively used for part-based recognition of scenes, people, animals and multi-part objects. To achieve tractability, the structure and param...
Benjamin Sapp, Chris Jordan, Ben Taskar
BMCBI
2008
145views more  BMCBI 2008»
13 years 8 months ago
Directed acyclic graph kernels for structural RNA analysis
Background: Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel ...
Kengo Sato, Toutai Mituyama, Kiyoshi Asai, Yasubum...
IJCNN
2007
IEEE
14 years 2 months ago
Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines
Abstract— The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best p...
Gavin C. Cawley, Nicola L. C. Talbot
ASPLOS
1994
ACM
14 years 17 days ago
Compiler Optimizations for Improving Data Locality
In the past decade, processor speed has become significantly faster than memory speed. Small, fast cache memories are designed to overcome this discrepancy, but they are only effe...
Steve Carr, Kathryn S. McKinley, Chau-Wen Tseng
ICASSP
2011
IEEE
13 years 5 days ago
A kernelized maximal-figure-of-merit learning approach based on subspace distance minimization
We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. In particular, a fixed,...
Byungki Byun, Chin-Hui Lee