Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational cost of re-training the classifier would be prohibitively large. ,  propose a novel method for efficiently incorporating such a data modification effect into the classifier without actually re-training it. The proposed method provides bounds on the unknown optimal classifier with the cost only proportional to the size of the data modification.  demonstrate through numerical experiments that the proposed method provides sufficiently tight bounds with negligible computational costs, especially when a small part of the dataset is modified in a large-scale classification problem.
['Hiroyuki Hanada', 'Atsushi Shibagaki', 'Jun Sakuma', 'Ichiro Takeuchi']
I am writing a research paper on a idea Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization
[{"from": "human", "value": "I am writing a research paper on a idea Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational cost of re-training the classifier would be prohibitively large. ,  propose a novel method for efficiently incorporating such a data modification effect into the classifier without actually re-training it. The proposed method provides bounds on the unknown optimal classifier with the cost only proportional to the size of the data modification.  demonstrate through numerical experiments that the proposed method provides sufficiently tight bounds with negligible computational costs, especially when a small part of the dataset is modified in a large-scale classification problem."}]