Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a computation of the Vγ dimension for regression in bounded subspaces of Reproducing Kernel Hilbert Spaces (RKHS) for the Support Vector Machine (SVM) regression Ɛ-insensitive loss function LƐ, and general Lp loss functions. Finiteness of the Vγ dimension is shown, which also proves uniform convergence in probability for regression machines in RKHS subspaces that use the LƐ or general Lp loss functions.  presents a novel proof of this result. It also presents a computation of an upper bound of the Vγ dimension under some conditions, that leads to an approach for the estimation of the empirical Vγ dimension given a set of training data.
['Theodoros Evgeniou', 'Massimiliano Pontil']
I am writing a research paper on a idea On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spaces
[{"from": "human", "value": "I am writing a research paper on a idea On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spaces"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a computation of the V\u03b3 dimension for regression in bounded subspaces of Reproducing Kernel Hilbert Spaces (RKHS) for the Support Vector Machine (SVM) regression \u0190-insensitive loss function L\u0190, and general Lp loss functions. Finiteness of the V\u03b3 dimension is shown, which also proves uniform convergence in probability for regression machines in RKHS subspaces that use the L\u0190 or general Lp loss functions.  presents a novel proof of this result. It also presents a computation of an upper bound of the V\u03b3 dimension under some conditions, that leads to an approach for the estimation of the empirical V\u03b3 dimension given a set of training data."}]