Paper Info
Title
Damaged ship unsinkability classification model based on fuzzy support vector machine
Abstract
When the ship is damaged after weapon attack, it is necessary for commanders to recognise its unsinkability grade quickly. Through unsinkability classification, we can know whether the ship will sink or not and its sinking probability. The unsinkability classification is a N-class pattern recognition problem. The fuzzy support vector machine (FSVM) is used to distinguish a certain unsinkability grade from other unsinkability grades firstly. Concerning the definition of fuzzy membership is critical in FSVM, the support vector data description (SVDD) is used to found fuzzy membership function. Through samples test, we found that FSVM of which fuzzy membership calculated through SVDD has better classification efficiency and precision.
Year
DOI
Venue
2010
10.1109/FSKD.2010.5569336
FSKD
Keywords
Field
DocType
fuzzy set theory,fsvm,marine engineering,fuzzy support vector machine,ships,unsinkability classification,pattern classification,n-class pattern recognition problem,damaged ship unsinkability classification model,fuzzy membership function,svdd,damaged ship,support vector data description,sinking probability,support vector machines,accuracy,kernel,optimization,pattern recognition
Kernel (linear algebra),Pattern recognition,Computer science,Fuzzy logic,Support vector machine,Fuzzy membership function,Fuzzy set,Artificial intelligence,Fuzzy support vector machine,Pattern recognition problem,Machine learning,Data description
Conference
Volume
ISBN
Citations 
4
978-1-4244-5931-5
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Yue Hou102.03
Jin-yun Pu201.35