Improved Binary Forward Exploration: Learning Rate Scheduling Method for Stochastic Optimization

A new gradient-based optimization approach by automatically scheduling the learning rate has been proposed recently, which is called Binary Forward Exploration (BFE). The Adaptive version of BFE has also been discussed thereafter. In this paper, the improved algorithms based on them will be investigated, in order to optimize the efficiency and robustness of the new methodology. This improved approach provides a new perspective to scheduling the update of learning rate and will be compared with the stochastic gradient descent, aka SGD algorithm with momentum or Nesterov momentum and the most successful adaptive learning rate algorithm e.g. Adam. The goal of this method does not aim to beat others but provide a different viewpoint to optimize the gradient descent process. This approach combines the advantages of the first-order and second-order optimizations in the aspects of speed and efficiency

Pattern Discrimination

This paper makes use of the preliminary analysis of the image, through its basic image features, to find the suitable model and algorithm. Using Matlab to process the image, and then using fuzzy clustering analysis, boundary matching model and other analysis, to achieve the purpose of solving the problem. In view of the first problem, the image is prepared for processing first, and the image is segmented in batches. Since the image given by the attachment is BMP form, we first use MATLAB to process the image given by the attachment, so that it is stored in the standard data storage format of Matlab. In view of problem two, we first define the submatrix by defining the search neutralization domain and the coverage domain, and then establish the boundary matching method model, and for the first time the left and right boundary of the matrix to be obtained and the left and right boundary of the obtained submatrix are matched