Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider several models, which employ gradient-based method as a core optimization tool. Experimental results re obtained in a real time environment during WCCI-2006 Performance Prediction Challenge. None of the models re proved to be absolutely best against all five datasets. Furthermore,  can exploit the actual difference beten different models and create an ensemble system as a complex of the base models where the balances may be regulated using special parameters or confidence levels. Overfitting is a usual problem in the situation when dimension is comparable with the sample size or even higher. Using mean-variance filtering  can reduce the difference beten training and test results significantly considering some features as a noise.
['Vladimir Nikulin']
I am writing a research paper on a idea Learning with Mean-Variance Filtering, SVM and Gradient-based Optimization
[{"from": "human", "value": "I am writing a research paper on a idea Learning with Mean-Variance Filtering, SVM and Gradient-based Optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider several models, which employ gradient-based method as a core optimization tool. Experimental results re obtained in a real time environment during WCCI-2006 Performance Prediction Challenge. None of the models re proved to be absolutely best against all five datasets. Furthermore,  can exploit the actual difference beten different models and create an ensemble system as a complex of the base models where the balances may be regulated using special parameters or confidence levels. Overfitting is a usual problem in the situation when dimension is comparable with the sample size or even higher. Using mean-variance filtering  can reduce the difference beten training and test results significantly considering some features as a noise."}]