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• W3172743879 abstract "Logistic regression has been widely used in artificial intelligence and machine learning due to its deep theoretical basis and good practical performance. Its training process aims to solve a large-scale optimization problem characterized by a likelihood function, where the gradient descent approach is the most commonly used. However, when the data size is large, it is very time-consuming because it computes the gradient using all the training data in every iteration. Though this difficulty can be solved by random sampling, the appropriate sampled examples size is difficult to be predetermined and the obtained could be not robust. To overcome this deficiency, we propose a novel algorithm for fast training logistic regression via adaptive sampling. The proposed method decomposes the problem of gradient estimation into several subproblems according to its dimension; then, each subproblem is solved independently by adaptive sampling. Each element of the gradient estimation is obtained by successively sampling a fixed volume training example multiple times until it satisfies its stopping criteria. The final estimation is combined with the results of all the subproblems. It is proved that the obtained gradient estimation is a robust estimation, and it could keep the objective function value decreasing in the iterative calculation. Compared with the representative algorithms using random sampling, the experimental results show that this algorithm obtains comparable classification performance with much less training time." @default.
• W3172743879 created "2021-06-22" @default.
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• W3172743879 date "2021-05-29" @default.
• W3172743879 modified "2023-10-17" @default.
• W3172743879 title "Fast Training Logistic Regression via Adaptive Sampling" @default.
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• W3172743879 doi "https://doi.org/10.1155/2021/9991859" @default.
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