Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Determining the kernel and error penalty parameters for support vector machines (SVMs) is very problem-dependent in practice. A popular method to deciding the kernel parameters is the grid search method. In the training process, classifiers are trained with different kernel parameters, and only one of the classifiers is required for the testing process. This makes the training process time-consuming.   propose using the inter-cluster distances in the feature spaces to choose the kernel parameters. Calculating such distance costs much less computation time than training the corresponding SVM classifiers; thus the proper kernel parameters can be chosen much faster. Experiment results show that the inter-cluster distance can choose proper kernel parameters with which the testing accuracy of trained SVMs is competitive to the standard ones, and the training time can be significantly shortened.
['Kuo-Ping Wu', 'Sheng-De Wang']
I am writing a research paper on a idea Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space
[{"from": "human", "value": "I am writing a research paper on a idea Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Determining the kernel and error penalty parameters for support vector machines (SVMs) is very problem-dependent in practice. A popular method to deciding the kernel parameters is the grid search method. In the training process, classifiers are trained with different kernel parameters, and only one of the classifiers is required for the testing process. This makes the training process time-consuming.   propose using the inter-cluster distances in the feature spaces to choose the kernel parameters. Calculating such distance costs much less computation time than training the corresponding SVM classifiers; thus the proper kernel parameters can be chosen much faster. Experiment results show that the inter-cluster distance can choose proper kernel parameters with which the testing accuracy of trained SVMs is competitive to the standard ones, and the training time can be significantly shortened."}]