A_Methodological_Approach_for_Pattern_Recognition_.pdf

— The concept of pattern recognition refers to classification of data patterns and distinguishing them into predefined set of classes. There are various methods for recognizing patterns studied under this paper. The objective of this review paper is t o summarize the methods used in various stages of a pattern recognition system and identify the best suitable technique with its advantages over other techniques to recognize the complex patterns along with other real-life applications.

IEEE Transactions on Neural Networks, 1997

Pattern classification using neural networks and statistical methods is discussed. We give a tutorial overview in which popular classifiers are grouped into distinct categories according to their underlying mathematical principles; also, we assess what makes a classifier neural. The overview is complemented by two case studies using handwritten digit and phoneme data that test the performance of a number of most typical neural-network and statistical classifiers. Four methods of our own are included: Reduced kernel discriminant analysis, the learning -nearest neighbors classifier, the averaged learning subspace method, and a version of kernel discriminant analysis. , in 1993. His current research interests include neural networks, pattern recognition, as well as computational and mathematical statistics, in particular nonparametric function estimation. His earlier work has also included functional analysis, geometric modeling, and computer aided design.

Pattern recognition is an innate cognitive process of matching information from the environment with the information stored in memory. Core methods are successful in many areas of numerical analysis, pattern recognition and machine learning. These are methods which generate an abstracting model from given observations (objects, measurements) in a training step, which subsequently allows generalizing statements for new observations. Various approaches are used to implement a pattern recognition system. In this paper we will discuss Statistical, Structural, hybrid and Neural Network based approach.

Anais do 6. Congresso Brasileiro de Redes Neurais, 2016

This work presents an application of neural networks in pattern classification. A new algorithm for automatic classification of data is presented. That algorithm make use of a competitive neural network to aid the classification process. The algorithm gets a data set D and segment it into clusters. The only a priori information given is a number of auxiliary centers and a threshold distance. The algorithm uses the Mahalanobis metrics to cluster the data and find itself the number of classes.

Sadhana, 1994

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed.

Decision Sciences, 1995

A number of recent studies have compared the performance of neural networks ("s) to a variety of statistical techniques for the classification problem in discriminant analysis. The empirical results of these comparative studies indicate that while NNs often outperform the more traditional statistical approaches to classification, this is not always the case. Thus, decision makers interested in solving classification problems are left in a quandary as to what tool to use on a particular data set. We present a new approach to solving classification problems by combining the predictions of a well-known statistical tool with those of an NN to create composite predictions that are more accurate than either of the individual techniques used in isolation. Subject Areas: Decision Support Systems and Statistical Techniques. *The authors would like to thank Melody Kiang for supplying one of the data sets used in this study and Loren Rees for his helpful suggestions in the early stages of this research.

In this paper, we discuss the principles of using back-propagation neural networks for pattern recognition. The basic information on neural networks learning is presented. A comparison is drawn between classification performance of the back-propagation network and other pattern recognition methods. Special attention is paid to preprocessing of the required data. The following problems are discussed in detail: choosing network topology, selecting proper values for learning coeficients, and estimating the quality of learning and recognition processes.

Lecture Notes in Electrical Engineering, 2014

Pattern recognition techniques are divided into categories of supervised, unsupervised and semi supervised. Supervised pattern recognition methods are utilized in the examination of various sources' chemical data such as sensor measurements, spectroscopy, and chromatography. The unsupervised classification techniques use algorithms to classify and analyze huge amounts of raster cells. Semi-Supervised Learning is an approach that is in the middle ground between supervised and unsupervised learning and guarantees to be better at classification by involving data that is unlabeled. In this paper, we tried to categories pattern recognition methods and explain about each of them and we compared supervised method with unsupervised method in terms of types and location of features.

Advanced Computing: An International Journal, 2011

There is a great scope of expansion in the field of Neural Network, as it can be viewed as massively parallel computing systems consisting of an extremely large number of simple processors with many interconnections. NN models attempt to use some organizational principles in a weighted directed graphs in which nodes are artificial neurons and directed edges are connections between neuron outputs and neuron inputs. The main characteristic of neural network is that they have the ability to learn complex non-linear input output relationships. A single artificial neuron is a simulation of a neuron (basic human brain cell) and scientists have tried to emulate the neuron in a form of artificial neuron called perceptron. Pattern recognition is one of the areas where the neural approach has been successfully tried. This study is concerned to see the journey of pattern recognition from algorithmic approach to neural network approach.

Data mining is the method of analyzing the large amounts of data stored in data warehouses. Data analysis is done by using various techniques such as clustering, classification, etc. These techniques include various algorithms. Since different algorithms results in different set of information, it is necessary to compare the performance of various algorithms. The performance can be analyzed based on accuracy and on various quality measures. In this paper, we analyzed the performance of two classification algorithms Neural Network based Pattern Recognition and k-Nearest Neighbor algorithm, using Matrix Laboratory tool, MATLAB. These algorithms are tested against the four different datasets. Their performance results are analyzed and presented.