menu
{ "item_title" : "Scalable Pattern Recognition Algorithms", "item_author" : [" Pradipta Maji", "Sushmita Paul "], "item_description" : "This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/3/31/905/629/3319056298_b.jpg", "price_data" : { "retail_price" : "109.99", "online_price" : "109.99", "our_price" : "109.99", "club_price" : "109.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Scalable Pattern Recognition Algorithms|Pradipta Maji

Scalable Pattern Recognition Algorithms : Applications in Computational Biology and Bioinformatics

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

This item is Non-Returnable

Details

  • ISBN-13: 9783319056296
  • ISBN-10: 3319056298
  • Publisher: Springer
  • Publish Date: April 2014
  • Dimensions: 9.2 x 6.1 x 0.9 inches
  • Shipping Weight: 1.7 pounds
  • Page Count: 304

Related Categories

You May Also Like...

    1

BAM Customer Reviews