Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userمقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA ) وطريقة تحليل التمايز الخطي لفيشر (Fisher - LDA )
903
0 0
0.0
(
0
)
Added by
University of Aleppo أطروحة دكتوراه
Publication date
2017
fields
Mathematical Statistics
and research's language is
العربية
Created by
Shamra Editor
Ask ChatGPT about the research
مقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA ) وطريقة تحليل التمايز الخطي لفيشر (Fisher - LDA )
No English abstract
References used
العتوم شفيق وأخرون 1995 - الأساليب الإحصائية الطبعة الأولى دار المناهج عمان الأردن
rate research
Read More
مقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA)وطريقة تحليل التمايز الخطي لفيشر (Fisher-LDA)
The basic step in this algori thm is determining the number of clusters
(K) then calculating the distance between each cluster
center and the elements of images to join each element
to the closest cluster depending on threshold distance.
In this paper we offer a new interactive method for solving Multiobjective linear
programming problems. This method depends on forming the model for reducing the
relative deviations of objective functions from their ideal standard, and dealing with
the
unsatisfying deviations of objective functions by reacting with decision maker.
The results obtained from using this method were compared with many interactive
methods as (STEM Method[6] – Improvement STEM Method[7] – Matejas-peric
Method[8]). Numerical results indicate that the efficiency of purposed method comparing
with the obtained results by using that methods at initial solution point and the other
interactive points with decision maker.
The purpose of this article is to shed light on the mechanism
and the procedures of a program that classifies an input face into
any of the six basic facial expressions, which are Anger, Disgust,
Fear, Happiness, Sadness and Surprise, in addition
to normal face.
This program works by apply PCA- principal component
analysis algorithm, which is applied of one side of the face, and
depends, on contrast to the traditional studies which rely on the
whole face, on three components: Eyebrows, Eyes and Mouth.
Those out-value are used to determine the facial feature array as
an input to the neural network, and the neural network is trained by
using the back-propagation algorithm. Note that the faces used in
this study belong to people from different ages and races.
this paper illustrates two methods of having a desired sectoral pattern using the
techniques of antenna synthesizing ,we will calculate the array excitation coefficients by
using Fourier transform and Woodward method , beside studying the effect of
increasing
the array elements on the desired radiation pattern and the side loop level at a constant
distance. Finally we will compare the results of the two methods showing their advantages
and disadvantages.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
