Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This approach aims to optimize the kernel parameters and to efficiently reduce the number of support vectors, so that the generalization error can be reduced drastically. The proposed methodology suggests the use of a new model selection criterion based on the estimation of the probability of error of the SVM classifier. For comparison,  considered two more model selection criteria: GACV ('Generalized Approximate Cross-Validation') and VC ('Vapnik-Chernovenkis') dimension. These criteria are algebraic estimates of upper bounds of the expected error. For the former,  also propose a new minimization scheme. The experiments conducted on a bi-class problem show that  can adequately choose the SVM hyper-parameters using the empirical error criterion. Moreover, it turns out that the criterion produces a less complex model with fer support vectors. For multi-class data, the optimization strategy is adapted to the one-against-one data partitioning. The approach is then evaluated on images of handwritten digits from the USPS database.
['Nedjem-Eddine Ayat', 'Mohamed Cheriet', 'Ching Y. Suen']
I am writing a research paper on a idea Automatic model selection for the optimization of SVM kernels
[{"from": "human", "value": "I am writing a research paper on a idea Automatic model selection for the optimization of SVM kernels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This approach aims to optimize the kernel parameters and to efficiently reduce the number of support vectors, so that the generalization error can be reduced drastically. The proposed methodology suggests the use of a new model selection criterion based on the estimation of the probability of error of the SVM classifier. For comparison,  considered two more model selection criteria: GACV ('Generalized Approximate Cross-Validation') and VC ('Vapnik-Chernovenkis') dimension. These criteria are algebraic estimates of upper bounds of the expected error. For the former,  also propose a new minimization scheme. The experiments conducted on a bi-class problem show that  can adequately choose the SVM hyper-parameters using the empirical error criterion. Moreover, it turns out that the criterion produces a less complex model with fer support vectors. For multi-class data, the optimization strategy is adapted to the one-against-one data partitioning. The approach is then evaluated on images of handwritten digits from the USPS database."}]