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» Covering Numbers for Support Vector Machines
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NIPS
2000
15 years 1 months ago
Vicinal Risk Minimization
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector M...
Olivier Chapelle, Jason Weston, Léon Bottou...
ML
2007
ACM
144views Machine Learning» more  ML 2007»
14 years 11 months ago
Invariant kernel functions for pattern analysis and machine learning
In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invarianc...
Bernard Haasdonk, Hans Burkhardt
ICRA
2006
IEEE
100views Robotics» more  ICRA 2006»
15 years 5 months ago
Learning EMG Control of a Robotic Hand: Towards Active Prostheses
— We introduce a method based on support vector machines which can detect opening and closing actions of the human thumb, index finger, and other fingers recorded via surface E...
Sebastian Bitzer, P. Patrick van der Smagt
ICPR
2008
IEEE
15 years 6 months ago
RANSAC-SVM for large-scale datasets
Support Vector Machines (SVMs), though accurate, are still difficult to solve large-scale applications, due to the computational and storage requirement. To relieve this problem,...
Kenji Watanabe, Takio Kurita
FCCM
1999
IEEE
111views VLSI» more  FCCM 1999»
15 years 4 months ago
Optimizing FPGA-Based Vector Product Designs
This paper presents a method, called multiple constant multiplier trees MCMTs, for producing optimized recon gurable hardware implementations of vector products. An algorithm for ...
Dan Benyamin, John D. Villasenor, Wayne Luk