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Machine Learning Applications (Emerging Trends)

List Price: $27.99
SKU:
9783110777055
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  • Product Details

    Author:
    Rik Das, Siddhartha Bhattacharyya, Sudarshan Nandy
    Format:
    Paperback
    Pages:
    153
    Publisher:
    De Gruyter (January 31, 2022)
    Language:
    English
    Audience:
    Professional and scholarly
    ISBN-13:
    9783110777055
    ISBN-10:
    3110777053
    Weight:
    9.12oz
    Dimensions:
    6.69" x 9.45"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20260510163322-20260511.xml
    Folder:
    TWO RIVERS
    List Price:
    $27.99
    Country of Origin:
    Germany
    Series:
    De Gruyter Frontiers in Computational Intelligence
    As low as:
    $24.07
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Pub Discount:
    60
    Imprint:
    De Gruyter
  • Overview

    The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.