Machine Learning Applications: Emerging Trends (De Gruyter Frontiers in Computational Intelligence, 5)

ISBN 10: 3110777053 / ISBN 13: 9783110777055

Edité par De Gruyter, 2022

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N° de réf. du vendeur ria9783110777055_new

Détails bibliographiques

• Titre: Machine Learning Applications: Emerging Trends (De Gruyter Frontiers in Computational Intelligence, 5)
• Éditeur: De Gruyter
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 3110777053
• ISBN à 13 chiffres: 9783110777055
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

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.

À propos de l?auteur

The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope to 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.