Sampling Techniques for Supervised or Unsupervised Tasks

Description

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 Front Matter ....Pages i-xiii Introduction to Sampling Techniques (Guillaume Chauvet)....Pages 1-21 Core-Sets: Updated Survey (Dan Feldman)....Pages 23-44 A Family of Unsupervised Sampling Algorithms (Serge Guillaume, Frédéric Ros)....Pages 45-81 From Supervised Instance and Feature Selection Algorithms to Dual Selection: A Review (Frédéric Ros, Serge Guillaume)....Pages 83-128 Approximating Spectral Clustering via Sampling: A Review (Nicolas Tremblay, Andreas Loukas)....Pages 129-183 Sampling Technique for Complex Data (A. Idarrou, H. Douzi)....Pages 185-203 Boosting the Exploration of Huge Dynamic Graphs (F. Javier Calle, Dolores Cuadra, Jesica Rivero, Pedro Isasi)....Pages 205-226 Back Matter ....Pages 227-232