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- W2384594732 abstract "Classification is a basic task in data mining and pattern recognition that requires the construction of a classifi- er,that is,a function that assigns a class label to instances described by a set of features(or attributes).Recently,a lot of new methods come forth,such as Fuzzy sets,Rough sets,Neural Network,Support Vector Machine and Genetic Algorithms,Ant Behavior Simulation,Case-based Reasoning,Bayesian Network etc..Since 1988 Pearl et al.provided the concept of Bayesian networks for the first time,it has been popular used in the Artificial Intelligence(AI)communi- ty.Bayesian networks are powerful formalism for representing and reasoning under conditions of uncertainty,and it is a powerful tool for knowledge representation and inference under conditions of uncertainty.However,Bayesian Network was not considered as classifiers until the discovery of Naive Bayesian Network,a simple kind of Bayesian Networks,which assumes the independent features given the class attribute(node)and is surprisingly effective.From that time on,it trig- gered experts to explore more deeply into Bayesian Networks as classifiers.Since theNaiveindependent assumption in Naive Bayesian Network can not be held in many cases,researchers have wondered whether the performance will be better if the strong independent assumption among features(or variables)were relaxed.In this paper,on the basis of the study of Naive Bayes Classifiers,the Na'lve Bayesian Network is generalized and is united with maximum likelihood classifier (MLC)on the mathematic model.To validate the feasibility and effectivity of the proposed method applied in the texture classification of aerial image,some aerial images of some cities in China are used in the experiments.The experiment results demonstrate that the generalized Bayesian Network-n-level Bayesian Network performs better in overall classifica- tion precision than maximum likelihood classifier and Naive Bayesian Network.The proposed method considers correlations among features,but the correlations are inherent.It is difficult to represent the correlations only by the parameter n.Dif- ferent values of n can get different experiment results,which will be researched deeply in the future." @default.
- W2384594732 created "2016-06-24" @default.
- W2384594732 creator A5075538321 @default.
- W2384594732 date "2008-01-01" @default.
- W2384594732 modified "2023-09-28" @default.
- W2384594732 title "Aerial Image Texture Classification Based on n-level Bayesian Network" @default.
- W2384594732 hasPublicationYear "2008" @default.
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