Prediction and Analysis for Knowledge Representation and Machine Learning

Prediction and Analysis for Knowledge Representation and Machine Learningdemonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book's website.

Features
Examines the representational adequacy of needed knowledge representationManipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original informationImproves inferential and acquisition efficiency by applying automatic methods to acquire new knowledgeCovers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technologyDescribes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter
This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which include both basic and advanced concepts.


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