Multi-Objective Machine Learning (Record no. 20054)

MARC details
000 -LEADER
fixed length control field 04853nam a22004935i 4500
001 - CONTROL NUMBER
control field 978-3-540-33019-6
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20170628034459.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 100301s2006 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783540330196
-- 978-3-540-33019-6
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/3-540-33019-4
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA329-348
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA640-643
072 #7 - SUBJECT CATEGORY CODE
Subject category code TBJ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MAT003000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Jin, Yaochu.
Relator term editor.
245 10 - TITLE STATEMENT
Title Multi-Objective Machine Learning
Medium [electronic resource] /
Statement of responsibility, etc edited by Yaochu Jin.
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg,
-- 2006.
300 ## - PHYSICAL DESCRIPTION
Extent XIV, 660 p. 254 illus.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
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347 ## -
-- text file
-- PDF
-- rda
490 1# - SERIES STATEMENT
Series statement Studies in Computational Intelligence,
International Standard Serial Number 1860-949X ;
Volume number/sequential designation 16
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Multi-Objective Clustering, Feature Extraction and Feature Selection -- Feature Selection Using Rough Sets -- Multi-Objective Clustering and Cluster Validation -- Feature Selection for Ensembles Using the Multi-Objective Optimization Approach -- Feature Extraction Using Multi-Objective Genetic Programming -- Multi-Objective Learning for Accuracy Improvement -- Regression Error Characteristic Optimisation of Non-Linear Models -- Regularization for Parameter Identification Using Multi-Objective Optimization -- Multi-Objective Algorithms for Neural Networks Learning -- Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming -- Multi-Objective Optimization of Support Vector Machines -- Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design -- Minimizing Structural Risk on Decision Tree Classification -- Multi-objective Learning Classifier Systems -- Multi-Objective Learning for Interpretability Improvement -- Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers -- GA-Based Pareto Optimization for Rule Extraction from Neural Networks -- Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems -- Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction -- Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model -- Multi-Objective Ensemble Generation -- Pareto-Optimal Approaches to Neuro-Ensemble Learning -- Trade-Off Between Diversity and Accuracy in Ensemble Generation -- Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks -- Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification -- Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection -- Applications of Multi-Objective Machine Learning -- Multi-Objective Optimisation for Receiver Operating Characteristic Analysis -- Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination -- Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle -- A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments -- Multi-Objective Neural Network Optimization for Visual Object Detection.
520 ## - SUMMARY, ETC.
Summary, etc Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Physics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering mathematics.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Appl.Mathematics/Computational Methods of Engineering.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Complexity.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783540306764
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Studies in Computational Intelligence,
-- 1860-949X ;
Volume number/sequential designation 16
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1007/3-540-33019-4">http://dx.doi.org/10.1007/3-540-33019-4</a>
912 ## -
-- ZDB-2-ENG
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Central Library Central Library 28/06/2017 Springer EBook   E-43233 28/06/2017 28/06/2017 E-Book

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