Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification algorithms based on different forms of support vector machines (SVMs) for dealing with interval-valued training data are proposed in the paper. L 2 -norm and L ∞ -norm SVMs are used for constructing the algorithms. The main idea allowing us to represent the complex optimization problems as a set of simple linear or quadratic programming problems is to approximate the Gaussian kernel by the ll-known triangular and Epanechnikov kernels. The minimax strategy is used to choose an optimal probability distribution from the set and to construct optimal separating functions. Numerical experiments illustrate the algorithms.
['Lev V. Utkin', 'Anatoly I. Chekh', 'Yulia A. Zhuk']
I am writing a research paper on a idea Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels
[{"from": "human", "value": "I am writing a research paper on a idea Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification algorithms based on different forms of support vector machines (SVMs) for dealing with interval-valued training data are proposed in the paper. L 2 -norm and L \u221e -norm SVMs are used for constructing the algorithms. The main idea allowing us to represent the complex optimization problems as a set of simple linear or quadratic programming problems is to approximate the Gaussian kernel by the ll-known triangular and Epanechnikov kernels. The minimax strategy is used to choose an optimal probability distribution from the set and to construct optimal separating functions. Numerical experiments illustrate the algorithms."}]