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ICCV
2009
IEEE
15 years 8 days ago
Max-Margin Additive Classifiers for Detection
We present methods for training high quality object detectors very quickly. The core contribution is a pair of fast training algorithms for piece-wise linear classifiers, which ...
Subhransu Maji, Alexander C. Berg
ICML
2010
IEEE
13 years 8 months ago
Learning the Linear Dynamical System with ASOS
We develop a new algorithm, based on EM, for learning the Linear Dynamical System model. Called the method of Approximated Second-Order Statistics (ASOS) our approach achieves dra...
James Martens
BC
1999
108views more  BC 1999»
13 years 7 months ago
Exact digital simulation of time-invariant linear systems with applications to neuronal modeling
An ecient new method for the exact digital simulation of time-invariant linear systems is presented. Such systems are frequently encountered as models for neuronal systems, or as s...
Stefan Rotter, Markus Diesmann
ICCAD
2006
IEEE
100views Hardware» more  ICCAD 2006»
14 years 4 months ago
Faster, parametric trajectory-based macromodels via localized linear reductions
— Trajectory-based methods offer an attractive methodology for automated, on-demand generation of macromodels for custom circuits. These models are generated by sampling the stat...
Saurabh K. Tiwary, Rob A. Rutenbar
CVPR
2006
IEEE
14 years 9 months ago
Incorporating the Boltzmann Prior in Object Detection Using SVM
In this paper we discuss object detection when only a small number of training examples are given. Specifically, we show how to incorporate a simple prior on the distribution of n...
Margarita Osadchy, Daniel Keren