Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, 1st Edition

  • Emilio Soria Olivas
  • Published By:
  • ISBN-10: 1605667676
  • ISBN-13: 9781605667676
  • DDC: 006.3
  • Grade Level Range: College Freshman - College Senior
  • 734 Pages | eBook
  • Original Copyright 2009 | Published/Released January 2011
  • This publication's content originally published in print form: 2009

  • Price:  Sign in for price

About

Overview

The machine learning approach provides a useful tool when the amount of data is very large and a model is not available to explain the generation and relation of the data set. The Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques provides a set of practical applications for solving problems and applying various techniques in automatic data extraction and setting. A defining collection of field advancements, this Handbook of Research fills the gap between theory and practice, providing a strong reference for academicians, researchers, and practitioners.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Page.
List of Contributors.
Table of Contents.
Detailed Table of Contents.
Foreword.
Preface.
1: Exploring the Unknown Nature of Data: Cluster Analysis and Applications.
2: Principal Graphs and Manifolds.
3: Learning Algorithms for RBF Functions and Subspace Based Functions.
4: Nature Inspired Methods for Multi-Objective Optimization.
5: Artifcial Immune Systems for Anomaly Detection.
6: Calibration of Machine Learning Models.
7: Classification with Incomplete Data.
8: Clustering and Visualization of Multivariate Time Series.
9: Locally Recurrent Neural Networks and Their Application.
10: Nonstationary Signal Analysis with Kernel Machines.
11: Transfer Learning.
12: Machine Learning in Personalized Anemia Treatment.
13: Deterministic Pattern Mining on Genetic Sequences.