Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

11

TSMC
2002

favoriteEmaildiscussreport

119views more TSMC 2002»

A cloning approach to classifier training

13 years 4 months ago

A cloning approach to classifier training

Download webfea-lb.fea.aub.edu.lb

The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for linear classifiers. In this paper, the algorithm is extended to multilayer neural networks which may be used as nonlinear classifiers. It is also shown that the application of the Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the back-propagation algorithm.

M. A. Al-Alaoui, R. Mouci, M. M. Mansour, Rony Fer

Real-time Traffic

Al-Alaoui Algorithm | Algorithm | TSMC 2002 | Weighted Mean-square Error |

claim paper

Related Content

» Training Binary GP Classifiers Efficiently A Paretocoevolutionary Approach

» Evaluation of Different Approaches to Training a Genre Classifier

» Learning kNearest Neighbor Naive Bayes for Ranking

» Learning to Classify Documents with Only a Small Positive Training Set

» Semantic Image Search and Subset Selection for Classifier Training in Object Recognition

» Constructing Diverse Classifier Ensembles using Artificial Training Examples

» Unifying discriminative visual codebook generation with classifier training for object cat...

» Interactively Training Pixel Classifiers

» Simultaneous Feature Selection and Classifier Training via Linear Programming A Case Study...

Post Info
More Details (n/a)

Added	23 Dec 2010
Updated	23 Dec 2010
Type	Journal
Year	2002
Where	TSMC
Authors	M. A. Al-Alaoui, R. Mouci, M. M. Mansour, Rony Ferzli

Comments (0)