This book describes in detail sampling techniques that can be used for
unsupervised and supervised cases, with a focus on sampling techniques
for machine learning algorithms. It covers theory and models of sampling
methods for managing scalability and the "curse of dimensionality",
their implementations, evaluations, and applications. A large part of
the book is dedicated to database comprising standard feature vectors,
and a special section is reserved to the handling of more complex
objects and dynamic scenarios. The book is ideal for anyone teaching or
learning pattern recognition and interesting teaching or learning
pattern recognition and is interested in the big data challenge. It
provides an accessible introduction to the field and discusses the state
of the art concerning sampling techniques for supervised and
unsupervised task.
- Provides a comprehensive description of sampling techniques for
unsupervised and supervised tasks;
- Describe implementation and evaluation of algorithms that
simultaneously manage scalable problems and curse of dimensionality;
- Addresses the role of sampling in dynamic scenarios, sampling when
dealing with complex objects, and new challenges arising from big
data.
"This book represents a timely collection of state-of-the art research
of sampling techniques, suitable for anyone who wants to become more
familiar with these helpful techniques for tackling the big data
challenge."
M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer
Science, University of Central Arkansas
"In science the difficulty is not to have ideas, but it is to make them
work"
From Carlo Rovelli
