Sheela Ramanna & Lakhmi C Jain 
Emerging Paradigms in Machine Learning [PDF ebook] 

Ủng hộ

This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.   

€96.29
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Mục lục

From the content: Emerging Paradigms in Machine Learning: An Introduction.- Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization.- Optimised information abstraction in granular Min/Max clustering.- Mining Incomplete Data—A Rough Set Approach.- Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation.

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Ngôn ngữ Anh ● định dạng PDF ● Trang 498 ● ISBN 9783642286995 ● Kích thước tập tin 17.6 MB ● Biên tập viên Sheela Ramanna & Lakhmi C Jain ● Nhà xuất bản Springer Berlin ● Thành phố Heidelberg ● Quốc gia DE ● Được phát hành 2012 ● Có thể tải xuống 24 tháng ● Tiền tệ EUR ● TÔI 2663498 ● Sao chép bảo vệ DRM xã hội

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