Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách keo gáy, Bìa mềm
Thể loại:Computers - Computer Science
Năm:2017
In lần thứ:2
Ngôn ngữ:english
Trang:481
This book provides comprehensive coverage of the
field of outlier analysis from a computer science point of view. It
integrates methods from data mining, machine learning, and statistics
within the computational framework and therefore appeals to multiple
communities. The chapters of this book can be organized into three
categories:
Basic algorithms: Chapters 1 through 7 discuss
the fundamental algorithms for outlier analysis, including probabilistic
and statistical methods, linear methods, proximity-based methods,
high-dimensional (subspace) methods, ensemble methods, and supervised
methods.
Domain-specific methods: Chapters 8 through 12 discuss
outlier detection algorithms for various domains of data, such as text,
categorical data, time-series data, discrete sequence data, spatial
data, and network data.
Applications: Chapter 13 is devoted to
various applications of outlier analysis. Some guidance is also provided
for the practitioner.
The second edition of this book is more
detailed and is written to appeal to both researchers and practitioners.
Significant new material has been added on topics such as kernel
methods, one-class support-vector machines, matrix factorization, neural
networks, outlier ensembles, time-series methods, and subspace methods.
It is written as a textbook and can be used for classroom teaching.
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