learning network model acquiring feature information of the obtained generated image by using the first learning network model; and updating weights of layers respectively included in the first and second learning network models, based on a difference between the feature information of the query image ...
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Objective : Transfer learning has been shown to be an eective strategy for pre-training a deep neural network (DNN) to boost performance on smaller data-sets. This is particularly useful when it is dicult or costly to acquire high quality data, such as in medicine. In this study we evalu...
意大利的RINA在谈到突袭行动和EC竞争局(DG Comp)的调查时说:“ DG Comp的调查并没有使IACS内的技术合作更加容易。但是,RINA与IACS有着良好的关系和沟通。” 著录项 来源 《Fairplay International Shipping Weekly》 |2008年第6503期|p.7|共1页 作者 MIRIAM FAHEY; 作者单位 收录信息 原文格式 PDF ...
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We also present an analysis of a novel approach, Class-Imbalanced Learning on Graphs (CILG) for fault localization, as an alternative to conventional methods of calculating program element suspiciousness scores. The proposed approach is trained and tested on a real-world dataset containing buggy ...
There are various methods to solve class imbalance and cost sensitive problems e.g, AdaBoost, CSSVM, KSVM, FSVM and Neural networks. We conducted experiments on various datasets using Neural Networks, Adaboost, Support vector machines, Cost sensitive SVM, Cost sensitive neural networks and Cost ...
Conditional normalizing flowSupervised machine learning models have been widely used to predict and get insight into diseases by classifying patients based on personal health records. However, a class imbalance is an obstacle that disrupts the training of the models. In this study, we aimed to ...
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