AINA
2010
IEEE
13 years 1 months ago
2010
IEEE
- This paper introduces a neural network training tool through computer networks. The following algorithms, such as neuron by neuron (NBN) [1][2], error back propagation (EBP), Lev...
IJDLS
2010
13 years 1 months ago
2010
Data acquisition is a major concern in text classification. The excessive human efforts required by conventional methods to build up quality training collection might not always b...
ICTIR
2009
Springer
13 years 2 months ago
2009
Springer
Abstract. In text classification (TC) and other tasks involving supervised learning, labelled data may be scarce or expensive to obtain; strategies are thus needed for maximizing t...
ICCV
2009
IEEE
13 years 2 months ago
2009
IEEE
A good training dataset, representative of the test images expected in a given application, is critical for ensuring good performance of a visual categorization system. Obtaining ...
HCI
2009
13 years 2 months ago
2009
A variety of software-based systems are being used as training media. There is not, however, an accepted approach to evaluating the usability of these systems. Traditional usabilit...
HCI
2009
13 years 2 months ago
2009
Simulation-based training in complex decision making often requires ample personnel for playing various roles (e.g. team mates, adversaries). Using intelligent agents may diminish ...
EMNLP
2009
13 years 2 months ago
2009
Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retr...
EMNLP
2009
13 years 2 months ago
2009
Current statistical machine translation (SMT) systems are trained on sentencealigned and word-aligned parallel text collected from various sources. Translation model parameters ar...
DRR
2009
13 years 2 months ago
2009
When is it safe to use synthetic data in supervised classification? Trainable classifier technologies require large representative training sets consisting of samples labeled with...
ACL
2009
13 years 2 months ago
2009
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...