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 Knowledgeable Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in research filed. The approaches are reviewed by real life examples from a wide range of research topics. An understanding of number of techniques and algorithms that are implemented in knowledge representation in machine learning are available through the book website.Features:

Examines the representational adequacy of needed knowledge representation.

Manipulates inferential appropriateness for knowledge representation in order to produce new knowledge derived from the original information.

Improving inferential and acquisition efficiency by applying automatic methods to acquire new knowledge.

Covering 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 h

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