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Books > Computing & IT > Applications of computing > Databases > Data mining
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Sampling Techniques for Supervised or Unsupervised Tasks (Hardcover, 1st ed. 2020)
Loot Price: R3,283
Discovery Miles 32 830
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Sampling Techniques for Supervised or Unsupervised Tasks (Hardcover, 1st ed. 2020)
Series: Unsupervised and Semi-Supervised Learning
Expected to ship within 10 - 15 working days
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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
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