Multivariate time series data often have a very high dimensionality. Classifying such high dimensional data poses a challenge because a vast number of features can be extracted. Furthermore, the meaning of the normally intuitive term "similar to" needs to be precisely defined. Representing the time series data effectively is an essential task for decision-making activities such as prediction, c...

In this study, the performance of two neural classifiers; namely Multi Layer Perceptron (MLP) and Radial Basis Fuction (RBF), are compared for a multivariate classification problem. MLP and RBF are two of the most widely neural network architecture in literature for classification and have successfully been employed for a variety of applications. A nonlinear scaling scheme for multivariate data...

a multivariate statistical analysis was performed on morphological characters of sixty-one populations of avena eriantha dur. a. clauda dur., a. barbata pott ex link., a. wiestii steud., a. fatua l., a. sterilis ssp. ludoviciana l. and a. sativa l. factor analysis revealed that intraspecific morphological variations are due to quantitative characters. interspecific as well as intersectional rel...

0

this experiment was conducted to investigate and evaluate the phenotypic and genotypic diversity of a population including of 94 doubled-haploids lines, their parents and five iranian local bread wheat (triticum aestivum l.) cultivars. thirteen agronomic and morphologic traits were recorded including; grain yield per unit area, harvest index, spike length, seed number per spike, spikelet densit...

Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-norm...

This paper presents an extension of m-mediods based modeling technique to cater for multimodal distributions of sample within a pattern. The classification of new samples and anomaly detection is performed using a novel classification algorithm which can handle patterns with underlying multivariate probability distributions. We have proposed two frameworks, namely MMC-ES and MMC-GFS, to enable ...

Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In...