Biomedical subjects

M N Vrahatis

Publications and source records attributed to M N Vrahatis.

3 recordsLinked to original sources

PubMed2006

Distributed computing methodology for training neural networks in an image-guided diagnostic application.

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

Algorithms↗

PubMed2005

Unsupervised clustering in mRNA expression profiles.

The development of microarray technologies gives scientists the ability to examine, discover and monitor the mRNA transcript levels of thousands of genes in a single experiment. Nonetheless, the tremendous amount of data that can be obtained from microarray studies presents a challenge for data analysis. The most commonly used computational approach for analyzing microarray data is cluster analysis, since the number of genes is usually very high compared to the number of samples. In this paper, we investigate the application of the recently proposed k-windows clustering algorithm on gene expression microarray data. This algorithm apart from identifying the clusters present in a data set also calculates their number and thus requires no special knowledge about the data. To improve the quality of the clustering, we employ various dimension reduction techniques and propose a hybrid one. The results obtained by the application of the algorithm exhibit high classification success.

Algorithms↗

PubMed1999

Improving the convergence of the backpropagation algorithm using learning rate adaptation methods.

This article focuses on gradient-based backpropagation algorithms that use either a common adaptive learning rate for all weights or an individual adaptive learning rate for each weight and apply the Goldstein/Armijo line search. The learning-rate adaptation is based on descent techniques and estimates of the local Lipschitz constant that are obtained without additional error function and gradient evaluations. The proposed algorithms improve the backpropagation training in terms of both convergence rate and convergence characteristics, such as stable learning and robustness to oscillations. Simulations are conducted to compare and evaluate the convergence behavior of these gradient-based training algorithms with several popular training methods.

Adaptation, Psychological↗