7 search results - page 1 / 2 » Absorbing Stochastic Estimator Learning Algorithms with High... |
AICCSA
2001
IEEE
13 years 8 months ago
2001
IEEE
NIPS
2008
13 years 5 months ago
2008
In this paper we aim to train deep neural networks for rapid visual recognition. The task is highly challenging, largely due to the lack of a meaningful regularizer on the functio...
DAGM
2007
Springer
13 years 10 months ago
2007
Springer
We present an approach for estimating the 3D position and in case of articulated objects also the joint configuration from segmented 2D images. The pose estimation without initial...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
We describe and analyze a simple and effective iterative algorithm for solving the optimization problem cast by Support Vector Machines (SVM). Our method alternates between stocha...
JMLR
2010
12 years 11 months ago
2010
Composite likelihood methods provide a wide spectrum of computationally efficient techniques for statistical tasks such as parameter estimation and model selection. In this paper,...