Mathematical analysis for machine learning and data mining
Material type:
TextPublication details: New Jersey World Scientific Description: 968 pISBN: 978000988898DDC classification: 006.3101515 Summary: MATHEMATICAL ANALYSIS FOR MACHINE
LEARNING AND DATA MINING
By
Dan Simovici (University
of Massachusetts, Boston,
USA)
ISBN 9780000988898
Extent: 984pp, PB
Pub Date: 2020
Price: Rs. 1795
Feel Books Pvt. Ltd.
4381/4 Ansari Road
Daryaganj
New Delhi 110002
Tel: +91 11 47472600
ABOUT THE BOOK
This compendium provides a self-contained introduction to mathematical
analysis in the field of machine learning and data mining. The mathematical
analysis component of the typical mathematical curriculum for computer
science students omits these very important ideas and techniques which are
indispensable for approaching specialized area of machine learning centered
around optimization such as support vector machines, neural networks, various
types of regression, feature selection, and clustering. The book is of special
interest to researchers and graduate students who will benefit from these
application areas discussed in the book.
| Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK
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Kannur University Central Library Stack | Stack | 006.3101515 SIM/M (Browse shelf (Opens below)) | Available | 58867 |
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| 006.31 OPT optimization for machine learning | 006.31 SAI/M Machine learning | 006.31 SHA/K Kernel methods for pattern analysis | 006.3101515 SIM/M Mathematical analysis for machine learning and data mining | 006.312 CHA/D Data mining methods | 006.312 DAT Data mining in bioinformatics / | 006.312 DAT Data mining :Next generation challenges and future generations |
MATHEMATICAL ANALYSIS FOR MACHINE
LEARNING AND DATA MINING
By
Dan Simovici (University
of Massachusetts, Boston,
USA)
ISBN 9780000988898
Extent: 984pp, PB
Pub Date: 2020
Price: Rs. 1795
Feel Books Pvt. Ltd.
4381/4 Ansari Road
Daryaganj
New Delhi 110002
Tel: +91 11 47472600
ABOUT THE BOOK
This compendium provides a self-contained introduction to mathematical
analysis in the field of machine learning and data mining. The mathematical
analysis component of the typical mathematical curriculum for computer
science students omits these very important ideas and techniques which are
indispensable for approaching specialized area of machine learning centered
around optimization such as support vector machines, neural networks, various
types of regression, feature selection, and clustering. The book is of special
interest to researchers and graduate students who will benefit from these
application areas discussed in the book.
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