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Full Description
A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system's perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems.
Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates 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 representation
Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information
Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge
Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology
Describes 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.
Contents
1. Machine Learning. 2. Design of a knowledge representation and Indexing: Background and Future. 3. Prediction Analysis of Noise Component using Median Based Filters Cascaded With Evolutionary Algorithms. 4. Construction of Deep Representations. 5. Knowledge Representation using Probabilistic model and Reconstruction based algorithms. 6. Multi-Ontology Mapping for Internet of Things (MOMI). 7. Higher Level Abstraction of Deep Architecture. 8. Knowledge Representation and Learning Mechanism Based on Networks of Spiking Neurons. 9. Multiview Representation learning. 10. Covid-19 Applications
