Library Services | Technical University of Kenya

Your cart is empty.
Normal view MARC view ISBD view

Minimum Error Entropy Classification [electronic resource] / by Joaquim P. Marques de Sá, Luís M.A. Silva, Jorge M.F. Santos, Luís A. Alexandre.

By: Marques de Sá, Joaquim P [author.].
Contributor(s): Silva, Luís M.A [author.] | Santos, Jorge M.F [author.] | Alexandre, Luís A [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 420Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XVIII, 262 p. 110 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642290299.Subject(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Statistical Physics, Dynamical Systems and ComplexityDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Continuous Risk Functionals -- MEE with Continuous Errors -- MEE with Discrete Errors -- EE-Inspired Risks -- Applications.
In: Springer eBooksSummary: This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

Introduction -- Continuous Risk Functionals -- MEE with Continuous Errors -- MEE with Discrete Errors -- EE-Inspired Risks -- Applications.

This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

There are no comments for this item.

Log in to your account to post a comment.

2017 | The Technical University of Kenya Library | +254(020) 2219929, 3341639, 3343672 | library@tukenya.ac.ke | Haile Selassie Avenue