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Extreme Learning Machine for Online Data Classification
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Publisher: Nala Kala
Pages: 112
Weight: 0.35
Height: 0.23 Width: 6.00 Depth: 9.00
Language: English
Extreme Learning Machine for Online Data Classification provides an accessible introduction to the principles, algorithms, and practical concepts behind Extreme Learning Machine (ELM) for online data classification. As machine learning applications increasingly rely on continuously generated data streams, efficient learning methods capable of rapid training and accurate classification have become essential. This book examines how Extreme Learning Machine techniques address these challenges through fast learning algorithms designed for dynamic and evolving datasets.
The book introduces the theoretical foundations of Extreme Learning Machine before exploring its role in online learning environments where data arrives continuously and models must adapt efficiently. It discusses the characteristics of online data classification, the importance of computational efficiency, and the practical considerations involved in developing scalable machine learning systems. Readers are introduced to concepts related to incremental learning, adaptive classification, and the performance characteristics that distinguish ELM from many traditional machine learning methods.
Designed for engineering and computer science audiences, the book presents machine learning concepts in a structured and organized manner suitable for academic study, professional reference, and self-learning. The material emphasizes the relationship between learning algorithms, computational performance, and classification accuracy while providing a clear overview of Extreme Learning Machine methodologies used for processing continuously evolving datasets.
The discussion also places Extreme Learning Machine within the broader field of artificial intelligence and data mining, highlighting its relevance for intelligent systems that require fast model training and efficient prediction. The content serves as a useful resource for understanding the theoretical principles that support modern online classification techniques while maintaining a practical engineering perspective.
Suitable for undergraduate and graduate students, researchers, instructors, software engineers, data scientists, and professionals interested in machine learning, this book offers a concise reference to the concepts, terminology, and methodologies associated with Extreme Learning Machine for online data classification. Its focus on computational efficiency, adaptive learning, and intelligent classification makes it valuable for readers seeking a practical understanding of fast machine learning algorithms and their role in modern data-driven applications.
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