image: There is no getting around it. Mathematical analysis is the gateway to the sciences and is central for understanding machine learning and data mining--especially when addressing topics such as the application of optimization techniques and functional analysis methods to support vector machines, or to measure theoretical arguments for approximation results needed in the study of neural networks. This book provides indispensable mathematical background for graduate students and researchers in machine learning and data mining. view more
Credit: World Scientific
There is no getting around it. Mathematical analysis is the gateway to the sciences and is central for understanding machine learning and data mining--especially when addressing topics such as the application of optimization techniques and functional analysis methods to support vector machines, or to measure theoretical arguments for approximation results needed in the study of neural networks.
Mathematical Analysis for Machine Learning and Data Mining is designed as a mathematical reference for students and researchers in machine learning and data mining. It helps scientists who have to grapple with huge quantities of data by providing detailed presentations of mathematical and applied aspects, as well as the indispensable mathematical background needed by graduate students and researchers in machine learning and data mining.
Mathematical Analysis for Machine Learning and Data Mining is intended for graduate students and researchers who have a basic mathematical education, and approaches various topics in machine learning that require functional analysis, measure theory and integration, optimization techniques, etc. The book's close integration between mathematics and computational aspects should also be of interest to mathematicians who seek an application area.
This book retails for US$198 / £175 in hardback. To know more about the book visit http://www.worldscientific.com/worldscibooks/10.1142/10702.
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About the Author
Dr. Dan Simovici is a Computer Science Professor at the University of Massachusetts Boston and the Editor-in-Chief of the Journal for Multiple-Valued Logic and Soft Computing. He the author or co-author of several books and more than 150 research publications. His research focuses currently on mathematical tools for data mining and machine learning.
About World Scientific Publishing Co.
World Scientific Publishing is a leading international independent publisher of books and journals for the scholarly, research and professional communities. World Scientific collaborates with prestigious organisations like the Nobel Foundation and US National Academies Press to bring high quality academic and professional content to researchers and academics worldwide. The company publishes about 600 books annually and 135 journals in various fields. To find out more about World Scientific, please visit http://www.worldscientific.com.
For more information, contact Amanda at heyun@wspc.com.