Introduction to Machine Learning and Bioinformatics (Computer Science and Data Analysis)

Lucidly Integrates Current Activities 
Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. 
Examines Connections between Machine Learning &amp; Bioinformatics 
The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. 
Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems 
Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

Introduction to Machine Learning and Bioinformatics (Chapman & Hall/ CRC Computer Science & Data Analysis)

Business & Money

Mitra, Sushmita

Datta, Sujay

Perkins, Theodore

Michailidis, George

Economics

Chapman and Hall/CRC

The neuro-fuzzy approach to pattern recognition-a unique overview Recent years have seen a surge of interest in neuro-fuzzy computing, which combines fuzzy logic, neural networks, and soft computing techniques. This book focuses on the application of this new tool to the rapidly evolving area of pattern recognition. Written by two leaders in neural networks and soft computing research, this landmark work presents a unified, comprehensive treatment of the state of the art in the field. The authors consolidate a wealth of information previously cattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern recognition tasks in the neuro-fuzzy network-classification, feature evaluation, rule generation, knowledge extraction, and hybridization. Special emphasis is given to the integration of neuro-fuzzy methods with rough sets and genetic algorithms (GAs) to ensure more efficient recognition systems. Clear, concise, and fully referenced, Neuro-Fuzzy Pattern Recognition features extensive examples and highlights key applications in speech, machine learning, medicine, and forensic science. It is an extremely useful resource for scientists and engineers in laboratories and industry as well as for anyone seeking up-to-date information on the advantages of neuro-fuzzy pattern recognition in new computer technologies.

Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing

Computers & Technology

Pal, Sankar K.

Computer Science

Wiley-Interscience

<ul>
<li>First title to ever present soft computing approaches and their application in data mining, along with the traditional hard-computing approaches</li> <li>Addresses the principles of multimedia data compression techniques (for image, video, text) and their role in data mining</li> <li>Discusses principles and classical algorithms on string matching and their role in data mining</li> </ul>

Data Mining: Multimedia, Soft Computing, and Bioinformatics

Acharya, Tinku

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. 
Examines Connections between Machine Learning &amp; Bioinformatics 
The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. 
Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems 
Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

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Books by Mitra, Sushmita