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Practice2

于 2020-12-17 发布文件大小:2KB

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根据学生两次考试成绩的数据来预测学生是否能被大学录取，用逻辑斯蒂回归算法实现，分别执行梯度下降算法、随机梯度下降算法、牛顿法(According student test scores twice to predict whether the student can be admitted to universities, implemented in logistic regression algorithm.)

文件列表：

Practice2
.........\ex4x.dat,2640,2010-10-16
.........\ex4y.dat,1360,2010-10-16
.........\gradientDescent.m,858,2016-11-15
.........\plotData.m,406,2016-11-04
.........\practice2.m,2410,2016-11-15

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Image Texture Classification Using Combined Grey Level Co-Occurrence Probabilities and Support Vector Machines Texture refers to properties that represent the surface or structure of an object and is defined as something consisting of mutually related elements. The main focus in this study is to do texture segmentation and classification for texture digital images. Grey level co-occurrence probabilities (GLCP) method is being used to extract features from texture image. Gaussian support vector machines (GSVM) have been proposed to do classification on the extracted features. A popular Brodatz texture album had been chosen to test out the result. In this study, a combined GLCP-GSVM shows an improvement over GLCP in terms of classification accuracy.

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2011-11-08 08:04:30下载

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Practice2

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