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ML
2007
ACM
192views Machine Learning» more  ML 2007»
15 years 5 months ago
Annealing stochastic approximation Monte Carlo algorithm for neural network training
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
Faming Liang
ICRA
2010
IEEE
158views Robotics» more  ICRA 2010»
15 years 4 months ago
Coping with imbalanced training data for improved terrain prediction in autonomous outdoor robot navigation
Abstract— Autonomous robot navigation in unstructured outdoor environments is a challenging and largely unsolved area of active research. The navigation task requires identifying...
Michael J. Procopio, Jane Mulligan, Gregory Z. Gru...
ECCV
2008
Springer
16 years 8 months ago
Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks
Abstract. Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchi...
Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric P. Xi...
ICPR
2000
IEEE
16 years 7 months ago
Automatic Training of Page Segmentation Algorithms: An Optimization Approach
Most page segmentation algorithms have userspecifiable free parameters. However, algorithm designers typically do not provide a quantitative/rigorous method for choosing values fo...
Song Mao, Tapas Kanungo
ICML
2007
IEEE
16 years 7 months ago
Piecewise pseudolikelihood for efficient training of conditional random fields
Discriminative training of graphical models can be expensive if the variables have large cardinality, even if the graphical structure is tractable. In such cases, pseudolikelihood...
Charles A. Sutton, Andrew McCallum