Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Support vector machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem. The inverse problem of SVM is how to split a given dataset into two clusters such that the margin beten the two clusters attains maximum. Due to the computational complexity, it is difficult to give an exact and feasible solution to the inverse problem.  makes an attempt to reduce the complexity of the inverse problem by clustering. It is demonstrated that the maximum margin beten the two clusters is equivalent to the distance beten the two closest points in convex hulls in the linearly separable case. For the inseparable case, the maximum margin beten the two sets is equivalent to the distance beten the two closest points in the reduced convex hulls.
['Xi-Zhao Wang', 'Shuxia Lu', 'Ruixian Zhu']
I am writing a research paper on a idea Solving SVM inverse problems based on clustering
[{"from": "human", "value": "I am writing a research paper on a idea Solving SVM inverse problems based on clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Support vector machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem. The inverse problem of SVM is how to split a given dataset into two clusters such that the margin beten the two clusters attains maximum. Due to the computational complexity, it is difficult to give an exact and feasible solution to the inverse problem.  makes an attempt to reduce the complexity of the inverse problem by clustering. It is demonstrated that the maximum margin beten the two clusters is equivalent to the distance beten the two closest points in convex hulls in the linearly separable case. For the inseparable case, the maximum margin beten the two sets is equivalent to the distance beten the two closest points in the reduced convex hulls."}]