Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/39128
Title: Evolutionary computation for statistical pattern recognition
Authors: Wang, Xiao
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2006
Source: Wang, X. (2006). Evolutionary computation for statistical pattern recognition. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Evolutionary computation as a general problem-solving technique has been extensively applied in statistical pattern recognition. Typically, evolutionary algorithms are developed to solve complex optimization and search problems involved in different folds of designing a recognition system, e.g. feature extraction, supervised classification and clustering. These problems are often characterized by high-dimensional search spaces with convoluted landscape, noisy data, and little information about the objective functions. Traditional optimization methods are not efficient in dealing with them and evolutionaryalgorithms are therefore introduced.
Description: 139 p.
URI: https://hdl.handle.net/10356/39128
DOI: 10.32657/10356/39128
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
WangXiao2006.pdfMain report18.13 MBAdobe PDFThumbnail
View/Open

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.