Prediction and Analysis for Knowledge Representation and Machine Learning

This book provides an overview of knowledge representation and machine learning techniques, with a focus on applications in information technology and computer science. It aims to attract researchers and practitioners from various fields and levels of expertise, offering a comprehensive guide to both basic and advanced concepts.

Format: Hardback
Length: 220 pages
Publication date: 31 January 2022
Publisher: Taylor & Francis Ltd

This book is a comprehensive guide to the techniques and structures used in knowledge representation and machine learning. It provides readers with an insightful understanding of the various challenges involved in this field and offers a structured approach to knowledge representation in machine learning.

The primary objective of this book is to attract the attention of researchers and practitioners working in the areas of information technology and computer science. It aims to provide a comprehensive coverage of both basic and advanced concepts related to knowledge representation in machine learning. In today's rapidly evolving world, the development of adaptive, robust, scalable, and reliable applications has become crucial. Moreover, designing solutions for everyday problems has become an increasingly important task.

This edited book will be of immense value to industry professionals, as well as beginners and high-level users seeking to learn the latest developments in this field. It offers a comprehensive overview of basic and advanced concepts, making it an excellent complement to other books available in the market.

One of the key strengths of this book is its strong focus on applications. It provides real-world examples and case studies that demonstrate the practical applications of knowledge representation and machine learning techniques. This makes it easier for readers to understand the practical implications of these concepts and apply them to their own work.

Furthermore, the book is written in a clear and concise manner, making it accessible to a wide range of readers. It includes detailed explanations, examples, and exercises to help readers grasp the material and apply it effectively.

In conclusion, this book is a valuable resource for anyone interested in knowledge representation and machine learning. It provides a comprehensive and practical guide to the techniques and structures used in this field, and offers a strong focus on applications. Whether you are a researcher, practitioner, or beginner, this book will help you gain a deeper understanding of these concepts and apply them to your work.

Weight: 590g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367649104