A statistical approach to neural networks for pattern recognition

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Artificial neural network is a mathematical model, as well as its software or hardware implementation, built on the principle of organization and functioning of biological neural networks — nerve cell networks of a living organism. This concept arose when studying the processes occurring in the brain, and when trying to simulate these processes. The first such attempt was the neural networks of W. McCulloch and W.

Artificial Neural Networks/Pattern Recognition

After the development of learning algorithms, the resulting models began to be used for practical purposes: in forecasting problems, for pattern recognition, in management tasks, etc. Artificial neural network is a system of connected and interacting simple processors artificial neurons. Such processors are usually quite simple especially in comparison with processors used in personal computers. Each processor of such a network only deals with the signals it periodically receives, and the signals it periodically sends to other processors.

Pattern recognition Theory/The basic of pattern recognition part 1(Hindi)

And, nevertheless, being connected to a sufficiently large network with controlled interaction, such separately simple processors together are able to perform rather complex tasks. Neural networks are not programmed in the usual sense of the word, they are trained.


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The possibility of learning is one of the main advantages of neural networks over traditional algorithms. Technically, training is to find the coefficients of connections between neurons.

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In the process of learning, the neural network is able to detect complex dependencies between input data and output, and also perform generalization. Genetic algorithm is a heuristic search is used for solving the optimization and simulation by random selection, combination and variation of desired parameters, using mechanisms similar to natural selection in nature.

Artificial neural network

It is a kind of evolutionary computation with the help of which optimization problems are solved using the methods of natural evolution, such as inheritance, mutations, selection and crossover. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering.

An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron MLP , which is the most widely used of the neural network models.

Artificial Neural Networks and Statistical Pattern Recognition, Volume 11 - 1st Edition

This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? Could the model be made more robust? Which points will have a high leverage? What are good starting values for the fitting algorithm? Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS r codes that are available on the book's related Web site.

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Review "This book is a good introduction to neural networks for a statistician.

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