In this paper an alternative procedure to NORMAL 07-81 is proposed [18], using slightly smaller sample dimensions (2x2x2 cm), and there are presented first experimental data on three different marble stones, characterised by X-ray powder diffraction (XRD).
The results reveal more pronounced effects for smaller sample dimensions and also suggest that most of the evolving dislocation structure is formed prior to the occurrence of large strain bursts.
Small samples with dimensions 30×5 mm or 60×20 mm can be characterized.
However, microarray data are known to have some features, such as high dimension, small sample, high noise, high redundancy, and skewed class distribution which is called class imbalance problem.
This paper presents a novel machine learning method, termed support vector machine (SVM), to obtain a global optimization model in conditions of large project dimensions, small sample sizes and non-linearity.
However, the results of RBF would be slightly unsatisfactory when dealing with small sample which has higher feature dimension and fewer numbers.
Analysis of microarray data is associated with the methodological problems of high dimension and small sample size.
Different variable selection methods have been employed for the analysis of high dimension and small sample size time-to-event data [7 10].
Various methods have been used for variable selection in high-dimension and small sample size cases with a single survival endpoint.
Generally speaking, support vector machine (SVM) is the best choice for classifying cancer microarray data because of its advantages, such as its high generalization capability, absence of local minima, and adaptability for high-dimension and small sample data [ 20].
