This paper is organized as follows. Section II introduces the basic operations ofthe standard BP and other fast learning algorithms that will be used in the ...
In this paper, this approach is further enhanced by proposing to divide the learning process into multiple phases, and different fast learning algorithms are ...
During the training, different fast learning algorithms will be used in different phases to improve the global convergence capability. Our performance ...
During the training, different fast learning algorithms will be used in different phases to improve the global convergence capability. Our performance ...
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This paper proposes an efficient acceleration technique, the backpropagation with adaptive learning rate and momentum term, which is based on the ...
The multi-phase method improves energy. By biasing the learner to the ... Coordinated man- agement of multiple interacting resources in chip multiprocessors: A ...
We propose a novel multi-phase hierarchical approach for independently forecasting each series in a hierarchy using machine learning.
This paper presents a meta-optimization framework to design stable and efficient multi-phase algorithms for fit- ting software reliability growth models. The ...
Nov 14, 2022 · Federated Learning (FL) is a decentralized learning method used to train machine learning algorithms. In FL, a global model iteratively collects ...
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This paper proposes an Enhanced Two-Phase method to solve these two problems to improve the performance of existing fast learning algorithms. The proposed ...