مقارنة بعض طرائق تقدير المعلمة التمهيدية لدوال اللب متعدد المتغيرات وتوظيفها في الدوال التمييزية مع تطبيق عملي == Comparison of Some Smoothing of Estimate Methods For Multivariate Kernel Functions And Employs Them In Discriminants Functions With Practical Application
Author name:
سكينة شامل جاسم
Supervisor name:
لقاء علي محمد العلوي
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Master
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T3733 - p.pdf
Abstract:
ان التحليل التمييزي (Discriminant Analysis ) غالبا ما يعاني من مشكلة صغر حجم العينة ( SSS ) Small Simple Size خاصة عندما يتم تطبيقها لتصنيف انماط عالية الابعاد مثل (التعرف على الوجه، حركة اليد وحركة الجينات الوراثية و...)، او عندما تكون البيانات لديها | The linear discriminant analysis often suffer from small sample size (sss) problem.especially when they are applied for the classification of high_dimenssional patterns. Such as (face recognition) or when data is distributed normal distribution but when they are non _ linear and has spread widely. We are dealing with this problem by using a technique of kernel discriminant analysis. because this technique depends on the kernel density estimation which belong to the class of estimates. it is commonly perform Avery important technique for visualizing data distribution and smoothing. It is unimportant to procedure in the explanatory data analysis, as that used in kernel discriminate analysis well known by researchers. To identify statistical. patterns. it is also known that these estimates are. Based mainly on the choice of bandwidth parameter end controlled on smoothing of estimation and to choice kernel function. has been the use of carton methods to estimate band width parameter it is : plugin methad Least square Cross validation method Smoothed Cross validation method Are selected as a parameter of bandwidth and use in kernel density estimation (KDE( and then employ them in kernel discriminant analysis (KDA) approach. Through the joint base for groups is a kernel discriminant rule (KDR) which rely heavily on density classified (f ?_j) and probability of prior (? ?_j).therefore the problem of multiple class(The use of several variables) will be more important and visible to be parameter of bandwidth is different when compared with various densities classified. Finally, it was real data from the general blood cancer disease and style simulation application, as it was reached that way (KDA - SCV) is best when using the above standard compared with other methods because it gave the misclassification rate least.in the practical side results showed in such a way that nine people were the first (n1... n9) of the first group are infected with disease among 50 people is infected and 12 people from the second group infected with the Lisu people living with and they (n45... n50 and n1... n6 ).
