Linear separability of data sets is one of the basic concepts in the theory of neural networks and pattern recognition. Data sets are often linearly separable because of their high dimensionality. Such is the case of genomic data, in which a small number of cases is represented in a space with extremely high dimensionality. An evaluation of linear separability of two data sets can be combined w...

In this paper are presented results obtained in a vowel recognition task applying fuzzy neural networks. The vowels, uttered from 10 speakers each in 1000 different contexts are recognized using as features the first three formant frequencies. The results obtained in this case show that fuzzyfication process improves the recognition rate of the classical variants. The paper is organized in 6 ch...

In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, w...

We propose a new method for indexing large image databases. The method incorporates neural network learning algorithms and pattern recognition techniques to construct an image pattern dictionary. Image retrieval is then formulated as a process of dictionary search to compute the best matching codeword, which in turn indexes into the database items. Experimental results are presented.

A texture–based weed classification method was developed. The method consisted of a low–level Gabor wavelets–based feature extraction algorithm and a high–level neural network–based pattern recognition algorithm. This classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass categories for spatially selective weed control. In...

as the t-wave section in electrocardiogram (ecg) illustrates the repolarisation phase of heart activity, the information which is accumulated in this section is so significant that can explain the proper operation of electrical activities in heart. long qt syndrome (lqt) and t-wave alternans (twa) have imperceptible effects on time and amplitude of t-wave interval. therefore, t-wave shapes of t...

Feature Extraction plays most crucial and important role in character recognition. The selection of stable and representative set of features is the main problem in pattern recognition. Because of font characteristics and style variation of machine printed Tamil characters, feature extraction remains a problem. Feature extraction involves reducing the amount of resources required to describe a ...

Much work on modelling pattern recognition by AI systems has focused on the stability and plasticity of a system’s ongoing response to novel inputs. This paper discusses a general learning mechanism for ART2 neural networks, which incorporates forgetting by long-term memory trace decay. Such approach enables the system to reuse memory resources and adapt to a huge in diversity or continually ch...

Hoppeld networks are neural architectures remarkable by architectural simplicity and exibility in usage. There are many areas in which Hoppeld networks have been successfully applied, including associative memories, pattern recognition and artiicial intelligence. This paper proposes a novel application of Hoppeld networks in image processing, as morphologic lters, and illustrates the concept wi...