Machine Learning Paradigms: Theory and Application

Buch | Hardcover

IX, 474 Seiten

2018
Springer International Publishing (Verlag)
978-3-030-02356-0 (ISBN)

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The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today's world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.

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Part I: Machine Learning in Feature Selection.- Hybrid Feature Selection Method Based On The Genetic Algorithm And Pearson Correlation Coe cient.- Weighting Attributes and Decision Rules through Rankings and Discretisation Parameters.- Greedy Selection of Attributes to be Discretised.- Part II: Machine Learning in Classification and Ontology.- Machine learning for Enhancement Land Cover and Crop Types Classification.

Erscheinungsdatum	23.12.2018
Reihe/Serie	Studies in Computational Intelligence
Zusatzinfo	IX, 474 p. 242 illus., 152 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	883 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik
Schlagworte	Computational Intelligence • Foundations of Machine Learning • machine learning • pattern recognition • Social Networks
ISBN-10	3-030-02356-7 / 3030023567
ISBN-13	978-3-030-02356-0 / 9783030023560
Zustand	Neuware