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Computational Intelligence is comparatively a new field but it has made a tremendous progress in virtually every discipline right from engineering, science, business, m- agement, aviation to healthcare. Computational intelligence already has a solid track-record of applications to healthcare, of which this book is a continuation. We would like to refer the reader to the excellent previous volumes in this series on computational intelligence in heal- care [1-3]. This book is aimed at providing the most recent advances and state of the art in the practical applications of computational intelligence paradigms in healthcare. It - cludes nineteen chapters on using various computational intelligence methods in healthcare such as intelligent agents and case-based reasoning. A number of fielded applications and case studies are presented. Highlighted are in particular novel c- putational approaches to the semantic management of health information such as in the Web 2.0, mobile agents such as in portable devices, learning agents capable of adapting to diverse clinical settings through case-based reasoning, and statistical - proaches in computational intelligence. This book is targeted towards scientists, application engineers, professors, health professionals, professors, and students. Background information on computational intelligence has been provided whenever necessary to facilitate the comprehension of a broad audience including healthcare practitioners.

• Sales Rank: #12605518 in Books
• Published on: 2012-11-17
• Released on: 2012-11-17
• Original language: English
• Number of items: 1
• Dimensions: 9.25" h x 1.11" w x 6.10" l, .0 pounds
• Binding: Paperback
• 464 pages

From the Back Cover
This book is a continuation of the volumes providing various per-spectives on computational intelligence in healthcare [1-3]. This book is aimed to provide a sample of the state of art in the prac-tical applications of computational intelligence paradigms in health-care. It includes nineteen chapters on using various computational intelligent paradigms in healthcare such as intelligent agents and case-based reasoning. A number of applications and case studies are presented. This book is targeted towards scientists, application engineers, pro-fessors, health professionals, professors and students.

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• Sales Rank: #1932501 in Books
• Published on: 2009-04-28
• Original language: English
• Number of items: 1
• Dimensions: 9.30" h x .70" w x 6.10" l, 1.00 pounds
• Binding: Hardcover
• 184 pages

About the Author

Bharatendra K. Rai is an assistant professor in the Charlton College of Business at the University of Massachusetts, North Dartmouth. An ASQ Six Sigma black belt, Dr. Rai has a vast amount of consulting and training experiences in the automotive, electronics, food, pharmaceutical, software, chemical, and defense industries.

Nanua Singh is the president of Rapid Global Business Solutions, Inc., Madison Heights, Michigan. Dr. Singh was previously a professor at the India Institute of Technology, Delhi; head of the Department of Industrial Engineering at the University of Windsor, Canada; and professor of manufacturing engineering at Wayne State University, Detroit, Michigan.

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Key Features

• Design algorithms in F# to tackle complex computing problems
• Be a proficient F# data scientist using this simple-to-follow guide
• Solve real-world, data-related problems with robust statistical models, built for a range of datasets

Book Description

The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs.

If you want to learn how to use F# to build machine learning systems, then this is the book you want.

Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.

What you will learn

• Use F# to find patterns through raw data
• Build a set of classification systems using Accord.NET, Weka, and F#
• Run machine learning jobs on the Cloud with MBrace
• Perform mathematical operations on matrices and vectors using Math.NET
• Use a recommender system for your own problem domain
• Identify tourist spots across the globe using inputs from the user with decision tree algorithms

About the Author

Sudipta Mukherjee was born in Kolkata and migrated to Bangalore. He is an electronics engineer by education and a computer engineer/scientist by profession and passion. He graduated in 2004 with a degree in electronics and communication engineering.

He has a keen interest in data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning at large. His first book on Data Structure using C has been received quite well. Parts of the book can be read on Google Books at http://goo.gl/pttSh. The book was also translated into simplified Chinese, available from Amazon.cn at http://goo.gl/lc536. This is Sudipta's second book with Packt Publishing. His first book, .NET 4.0 Generics (http://goo.gl/MN18ce), was also received very well. During the last few years, he has been hooked to the functional programming style. His book on functional programming, Thinking in LINQ (http://goo.gl/hm0lNF), was released last year. Last year, he also gave a talk at @FuConf based on his LINQ book (https://goo.gl/umdxIX). He lives in Bangalore with his wife and son.

Sudipta can be reached via e-mail at sudipto80@yahoo.com and via Twitter at @samthecoder.

Table of Contents

Introduction to Machine Learning

Linear Regression

Classification Techniques

Information Retrieval

Collaborative Filtering

Sentiment Analysis

Anomaly Detection

• Sales Rank: #755685 in eBooks
• Published on: 2016-02-25
• Released on: 2016-02-25
• Format: Kindle eBook

About the Author

Sudipta Mukherjee

Sudipta Mukherjee was born in Kolkata and migrated to Bangalore. He is an electronics engineer by education and a computer engineer/scientist by profession and passion. He graduated in 2004 with a degree in electronics and communication engineering. He has a keen interest in data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning at large. His first book on Data Structure using C has been received quite well. Parts of the book can be read on Google Books at http://goo.gl/pttSh. The book was also translated into simplified Chinese, available from Amazon.cn at http://goo.gl/lc536. This is Sudipta's second book with Packt Publishing. His first book, .NET 4.0 Generics (http://goo.gl/MN18ce), was also received very well. During the last few years, he has been hooked to the functional programming style. His book on functional programming, Thinking in LINQ (http://goo.gl/hm0lNF), was released last year. Last year, he also gave a talk at @FuConf based on his LINQ book (https://goo.gl/umdxIX). He lives in Bangalore with his wife and son. Sudipta can be reached via e-mail at sudipto80@yahoo.com and via Twitter at @samthecoder.

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