Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction and feature extraction. Feature extraction and pattern classification can be conducted independently or jointly. Two popular independent feature extraction algorithms are Linear Discriminant Analysis (LDA) and Principal Compon...

In this paper we first introduce the concept of classification confidence in fuzzy rule-based classification. Classification confidence shows the strength of classification for an unseen pattern. Low classification confidence for an unseen pattern means that the classification of that pattern is not very clear compared to that with high classification confidence. Then we focus on the minimum cl...

Missing input data is a common drawback in many real-life pattern classification scenarios. The ability of missing data handling has become a fundamental requirement for pattern classification because an inappropriate treatment may cause large errors or false results on classification. The absence of certain values for relevant data attributes can seriously affect the accuracy of classification...

INTRODUCTION: Subjective facial analysis is a diagnostic method that provides morphological analysis of the face. Thus, the aim of the present study was to compare the facial and dental diagnoses and investigate their relationship. METHODS: This sample consisted of 151 children (7 to 13 years old), without previous orthodontic treatment, analyzed by an orthodontist. Standardized extraoral an...

Pattern Recognition is one of the very important and actively searched trait or branch of artificial intelligence. It is the science which tries to make machines as intelligent as human to recognize patterns and classify them into desired categories in a simple and reliable way. This review paper introduces the basic concepts of pattern recognition, the underlying system architecture and provid...

Human observers were trained to criterion in classifying compound Gabor signals with symmetry relationships, and were then tested with each of 18 blob-only versions of the learning set. Generalization to dark-only and light-only blob versions of the learning signals, as well as to dark-and-light blob versions was found to be excellent, thus implying virtually perfect generalization of the abili...