Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   investigate connections beten statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds  construct "compression coefficients" for SVMs which measure the amount by which the training labels can be compressed by a code built from the separating hyperplane. The main idea is to relate the coding precision to geometrical concepts such as the width of the margin or the shape of the data in the feature space. The so derived compression coefficients combine ll known quantities such as the radius-margin term R2/ρ2, the eigenvalues of the kernel matrix, and the number of support vectors. To test whether they are useful in practice  ran model selection experiments on benchmark data sets. As a result  found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized.
['Ulrike von Luxburg', 'Olivier Bousquet', 'Bernhard Schölkopf']
I am writing a research paper on a idea A Compression Approach to Support Vector Model Selection
[{"from": "human", "value": "I am writing a research paper on a idea A Compression Approach to Support Vector Model Selection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   investigate connections beten statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds  construct \"compression coefficients\" for SVMs which measure the amount by which the training labels can be compressed by a code built from the separating hyperplane. The main idea is to relate the coding precision to geometrical concepts such as the width of the margin or the shape of the data in the feature space. The so derived compression coefficients combine ll known quantities such as the radius-margin term R2/\u03c12, the eigenvalues of the kernel matrix, and the number of support vectors. To test whether they are useful in practice  ran model selection experiments on benchmark data sets. As a result  found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized."}]