Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods  use can be applied in the regression framework as ll as in the classification one when the classifier is obtained by thresholding a real-valued function.  study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular  focus on Hilbert space regularization and Kullback-Leibler regularization.  demonstrate how to apply the results to SVM for regression and classification.
['Olivier Bousquet', 'André Elisseeff']
I am writing a research paper on a idea Stability and generalization
[{"from": "human", "value": "I am writing a research paper on a idea Stability and generalization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods  use can be applied in the regression framework as ll as in the classification one when the classifier is obtained by thresholding a real-valued function.  study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular  focus on Hilbert space regularization and Kullback-Leibler regularization.  demonstrate how to apply the results to SVM for regression and classification."}]