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| #2461544 in Books | 2014-06-23 | Original language:English | PDF # 1 | 9.72 x1.14 x6.85l,3.00 | File type: PDF | 572 pages||12 of 13 people found the following review helpful.| Worst book in machine learning ever|By Jason W|I would give this book a minus 1 star if had this option. I couldn't believe it was a serious work of someone teaching at a school as prestigious as Princeton.
I am a CS grad student studying machine learning at another top university. I got interested in kernel methods recently, so (unfortunately!) I bought this book|About the Author|S. Y. Kung is a Professor in the Department of Electrical Engineering at Princeton University. His research areas include VLSI array/parallel processors, system modeling and identification, wireless communication, statistical signal processing,
Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With ove...
You easily download any file type for your gadget.Kernel Methods and Machine Learning | S. Y. Kung. I was recommended this book by a dear friend of mine.
