Empirical Study of Extreme Learning Machines for Clustering and Classification

"Empirical Study of Extreme Learning Machines for Clustering and Classification" by Al Shamiri is a comprehensive book that explores the application of extreme learning machines (ELMs) for clustering and classification tasks. ELMs are a type of machine learning algorithm that are known for their fast learning speed and high accuracy. The book presents an in-depth empirical study of ELMs, examining their performance in comparison to other popular machine learning algorithms such as support vector machines (SVMs) and deep learning networks.

The author provides a detailed analysis of various ELM-based clustering and classification models, highlighting their strengths and weaknesses. The book also includes real-world case studies, showcasing how ELMs have been applied in various domains such as finance, healthcare, and image processing.

This book is ideal for researchers, academics, and professionals working in the field of machine learning, as well as students pursuing advanced degrees in computer science, data science, and related fields. It provides a comprehensive understanding of ELMs, their applications, and their potential for future research and development. The empirical approach taken by the author adds credibility to the book and makes it an invaluable resource for anyone interested in using ELMs for clustering and classification.