Exploring Neural Networks with C#

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical.Exploring Neural Networks with C# presents the important properties of neural networks while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand.Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks.Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programyandlang=en

Computers & Technology

Chaki, Nabendu

Tadeusiewicz, Ryszard

Chaki, Rituparna

Computer Science

CRC-Press

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations―making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical. Exploring Neural Networks with C# presents the important properties of neural networks―while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand. Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks. Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&amp;lang=en

This book provides information about different types of dimensionality reduction (DR) methods and their effectiveness in hyperspectral data processing. The authors first explain how hyperspectral imagery (HSI) plays an important role in remote sensing due to its high spectral resolution that enables better identification of different materials on&nbsp;the earth's&nbsp;surface. The authors go on to describe potential challenges due to HSI being acquired in hundreds of narrow and contiguous bands, represented as a 3-dimensional image cube, often causing the bands to contain information redundancy. They then show how processing&nbsp;a&nbsp;large number of bands adds challenges in terms of computation complexity that reduces efficiency. The authors then present how DR is an essential step in hyperspectral data analysis to solve these issues. Overall, the book helps readers understand the DR processes and its&nbsp;impact&nbsp;in effective HSI analysis.

Dimensionality Reduction of Hyperspectral Imagery

Paul, Arati

Springer

The book focuses on an image processing technique known as binarization. It provides a comprehensive survey over existing binarization techniques for both document and graphic images. A number of evaluation techniques have been presented for quantitative comparison of different binarization methods. The book provides results obtained comparing a number of standard and widely used binarization algorithms using some standard evaluation metrics. The comparative results presented in tables and charts facilitates understanding the process. In addition to this, the book presents techniques for preparing a reference image which is very much important for quantitative evaluation of the binarization techniques. The results are produced taking image samples from standard image databases.

Exploring Image Binarization Techniques (Studies in Computational Intelligence, 560)

Shaikh, Soharab Hossain

Saeed, Khalid

The book presents various state-of-the-art approaches for process synchronization in a distributed environment. The range of algorithms discussed in the book starts from token based mutual exclusion algorithms that work on tree based topology. Then there are interesting solutions for more flexible logical topology like a directed graph, with or without cycle. In a completely different approach, one of the chapters presents two recent voting-based DME algorithms. All DME algorithms presented in the book aim to ensure fairness in terms of first come first serve (FCFS) order among equal priority processes. At the same time, the solutions consider the priority of the requesting processes and allocate resource for the earliest request when no such request from a higher priority process is pending.

Concurrency Control in Distributed System Using Mutual Exclusion (Studies in Systems, Decision and Control, 116)

Chattopadhyay, Samiran

Kanrar, Sukhendu

Networking & Cloud Computing

See the best price to sell, buy, or rent books by Chaki, Nabendu. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Chaki, Nabendu

See the best price to sell, buy, or rent books by Chaki, Nabendu. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Exploring Neural Networks with C#

Exploring Neural Networks with C#

Dimensionality Reduction of Hyperspectral Imagery

Exploring Image Binarization Techniques (Studies in Computational Intelligence, 560)

Exploring Image Binarization Techniques (Studies in Computational Intelligence, 560)

Concurrency Control in Distributed System Using Mutual Exclusion (Studies in Systems, Decision and Control, 116)