دانلود کتاب روش‌های هسته برای یادگیری ماشین با ریاضی و R

Among machine learning methods, kernels have always been a particular weakness of mine. I tried to read “Introduction to Kernel Methods” by Kenji Fukumizu (in Japanese) but failedmany times. I invited Prof. Fukumizu to give an intensive lecture at Osaka University and listened to the course for a week with the students, but I could not understand the book’s essence. When I first started writing this book, my goal was to rid my sense of weakness. However, now that this book is completed, I can tell readers how they can overcome their own kernel weaknesses. Most people, even machine learning researchers, do not understand kernels and use them. If you open this page, I believe you have a positive feeling that you want to overcome your weakness. The shortest path I would most recommend for achieving this is to learn mathematics by starting from the basics.Kernelswork according to themathematics behind them. It is essential to think through this concept until you understand it. The mathematics needed to understand kernels are called functional analysis (Chap. 2). Even if you know linear algebra or differential and integral calculus, you may be confused. Vectors are finite dimensional, but a set of functions is infinite dimensional and can be treated as linear algebra. If the concept of completeness is new to you, I hope you will take the time to learn about it. However, if you get through this second chapter, I think you will understand everything about kernels. This book is the third volume (of six) in the 100 Exercises for Building Logic set. Since this is a book, there must be a reason for publishing it (the so-called cause) when existing books on kernels can be found. The following are some of the features of this book.