Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  target the problem of model selection for Support Vector Classifiers through in–sample methods, which are particularly appealing in the small–sample regime, i.e. when few high–dimensional patterns are available. In particular,  describe the application of a trimmed hinge loss function to Rademacher Complexity and Maximal Discrepancy based in–sample approaches.  also show that the selected classifiers outperform the ones obtained with other state-of-the-art in-sample and out–of–sample model selection techniques in classifying Human Gene Expression datasets.
['Luca Oneto', 'Davide Anguita', 'Alessandro Ghio', 'Sandro Ridella']
I am writing a research paper on a idea Rademacher complexity and structural risk minimization: an application to human gene expression datasets
[{"from": "human", "value": "I am writing a research paper on a idea Rademacher complexity and structural risk minimization: an application to human gene expression datasets"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  target the problem of model selection for Support Vector Classifiers through in\u2013sample methods, which are particularly appealing in the small\u2013sample regime, i.e. when few high\u2013dimensional patterns are available. In particular,  describe the application of a trimmed hinge loss function to Rademacher Complexity and Maximal Discrepancy based in\u2013sample approaches.  also show that the selected classifiers outperform the ones obtained with other state-of-the-art in-sample and out\u2013of\u2013sample model selection techniques in classifying Human Gene Expression datasets."}]