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Deep learning with a large number of parametersrequires distributed training, where model accuracy and runtimeare two important factors to be considered. However, there hasbeen no systematic study of the tradeoff between these two factorsduring the model training process. This paper presents Rudra, aparameter server based distributed computing framework tunedfor training large-scale deep neural networks...
Policy of national student loans accelerates the reform of higher education in China and the process of market mechanism of talents training in a very great degree, and provides the important guarantee for the poor college students. However, at present, high default rate makes commercial bank which provides student loans bear the risk of bad debt, and affects the policy of national student loan to...
The solution of multi-output LS-SVR machines follows from solving a set of linear equations. Compared with ε-intensive SVR, it loses the advantage of a sparse decomposition. In order to limit the number of support vectors and reduce the computation cost, this paper presents a decremental recursive algorithm for multi-output LS-SVR machines. This algorithm removes one sample one time and large-scale...
In this paper, a new learning method tolerant to imprecision is introduced to fuzzy tree (FT) modeling method. The learning method is called ε-insensitive learning or ε learning, where, in order to fit the FT model to real data, the ε-insensitive loss function is used. FT method adaptively partitions the input space and is irrelevant to the dimension of the input space. For the consequent parameters,...
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