Sciweavers

801 search results - page 22 / 161
» The Inefficiency of Batch Training for Large Training Sets
Sort
View
ICANN
2010
Springer
13 years 9 months ago
Accelerating Large-Scale Convolutional Neural Networks with Parallel Graphics Multiprocessors
Training convolutional neural networks (CNNs) on large sets of high-resolution images is too computationally intense to be performed on commodity CPUs. Such architectures however ...
Dominik Scherer, Hannes Schulz, Sven Behnke
COLING
2000
13 years 10 months ago
Estimation of Stochastic Attribute-Value Grammars using an Informative Sample
We argue that some of the computational complexity associated with estimation of stochastic attributevalue grammars can be reduced by training upon an informative subset of the fu...
Miles Osborne
ICCV
2011
IEEE
12 years 8 months ago
Incremental On-line Semi-supervised Learning for Segmenting the Left Ventricle of the Heart from Ultrasound Data
Recently, there has been an increasing interest in the investigation of statistical pattern recognition models for the fully automatic segmentation of the left ventricle (LV) of t...
Gustavo Carneiro, Jacinto C. Nascimento
KDD
2008
ACM
167views Data Mining» more  KDD 2008»
14 years 9 months ago
A sequential dual method for large scale multi-class linear svms
Efficient training of direct multi-class formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as w...
S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang...
DRR
2009
13 years 6 months ago
Using synthetic data safely in classification
When is it safe to use synthetic data in supervised classification? Trainable classifier technologies require large representative training sets consisting of samples labeled with...
Jean Nonnemaker, Henry Baird