The automatic classification of vectorcardiograms and electrocardiograms into disease classes using computerized pattern recognition techntques has been a much studied problem. To date, however, no system exists which meets desired accuracy and noise immunity requirements and development of new techniques continues. An important aspect of the problem is that of feature selection, in which the f...

The problem of studying pattern recognition techniques for analyzing textured surfaces is considered in this thesis and the results are applied to the classification of steel surfaces according to their surface properties. Various texture analysis techniques are studied and features are extracted from steel surfaces. Two new texture analysis methods are introduced and tested. To simplify and en...

The present paper explores the supervised pattern recognition problem when binary features are used in pattern descriptions. The concept of Non-Reducible Descriptors (NRDs) for binary features is introduced. NRDs are descriptors of patterns that do not contain any redundant information. They correspond to syndromes in medical diagnosis and are represented as conjunctions. The proposed approach ...

Due to the high computation complexity and intra-class variance in the area of image pattern recognition, feature extraction for image pattern recognition has been the focus of interest for quite some time. In this paper, a novel feature extraction framework is presented, which first constructs an over-complete feature combination set, and then selects effective combinations by using feature se...

Feature selection and feature creating are two of the most important and difficult tasks in the field of pattern recognition. It involves determining a good feature subset given a set of candidate features. The acoustic analysis of vibration signals in the time and frequency domain frequently generates a large number of features and makes a reduction of dimensionality necessary. The present met...

Feature selection algorithm plays a crucial role in intrusion detection, data mining and pattern recognition. According to some evaluation criteria, it gets optimal feature subset by deleting unrelated and redundant features of the original data set. Aiming at solving the problems about the low accuracy, the high false positive rate and the long detection time of the existing feature selection ...