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KNN algorithm is the most usable classification algorithm, it is simple, straight and effective. But KNN can not identify the effect of attributes in dataset. For non-Gaussian distribution or non-Elliptic distribution, KNN can not solve these two kinds of problem effectively. A major approach to tackle this problem is to give each of the rest of attributes a weight value according to the relationship...
This paper presents a novel machine learning model-kernel granular support vector machine (KGSVM), which combines traditional support vector machine (SVM) with granular computing theory. By dividing granules and replacing with them in kernel space, the datasets can be reduced effectively without changing data distribution. And then the generalization performance and training efficiency of SVM can...
In Kernel Space, Support Vectors selection is an important issue for Support Vector Machines (SVMs). But, at present most sample selection methods have a common disadvantage that the candidate set for Support Vectors is the whole sample space, so, it may select interior samples or ldquooutliersrdquo that have little or even bad effect on the classifying quality. To tackle it, two improved methods...
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