Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the increase of the training set?s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel pre-extracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influence on the optimization result.  adopt a non-parametric k-NN rule called relative neighborhood graph (RNG) to extract the probable SVs from all the training samples. Experimental results verify that the approach proposed can effectively reduce training set?s size and accelerate the learning speed. At the same time, the classification accuracies are still competitive.
['Deqiang Han', 'Chongzhao Han', 'Yi Yang', 'Yu Liu', 'Wentao Mao']
I am writing a research paper on a idea Pre-extracting method for SVM classification based on the non-parametric K-NN rule
[{"from": "human", "value": "I am writing a research paper on a idea Pre-extracting method for SVM classification based on the non-parametric K-NN rule"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the increase of the training set?s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel pre-extracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influence on the optimization result.  adopt a non-parametric k-NN rule called relative neighborhood graph (RNG) to extract the probable SVs from all the training samples. Experimental results verify that the approach proposed can effectively reduce training set?s size and accelerate the learning speed. At the same time, the classification accuracies are still competitive."}]