·the sampling process adaptive The sampling refers to the prototype selection in Softmax layer and the data sampling inthedatalayer Firstly indeepmetriclearning hardexam ple mining is an important part to improve the training effi ciency and performance of the model Zhu et al [47] show that both verification loss and classification loss
·Section 2 introduces some studies about FIs mining through sampling and information granulation Section 3 covers some definitions and knowledge used in the paper Section 4 describes the SG in detail Section 5 shows some results of experiments on SG and gives some discussions and Section 6 contains conclusions
Contrastive learning has recently emerged as a powerful technique for graph self supervised pretraining GSP By maximizing the mutual information MI between a positive sample pair the network is forced to extract discriminative information from graphs to generate high quality sample representations However we observe that in the process of MI maximization Infomax the
·The paper is organized as follows Section 2 introduces some studies about FIs mining through sampling and information granulation Section 3 covers some definitions and knowledge used in the paper Section 4 describes the SG in detail Section 5 shows some results of experiments on SG and gives some discussions and Section 6 contains conclusions
·Training with such inconsistent negatives may confuse detectors in distinguishing between foreground and background and thus makes training less effective In this paper we propose a consistent negative sample mining method to filter out biased negatives in training Specifically the neural network takes the regression performance into
·3 Random sampling for data mining Random sampling is a method of selecting n units out of a total N such that every one of the CN n distinct samples has an equal chance of being selected In this paper we consider sequential random sampling without replacement the records are selected in the same orderas theyappear
·Due to the imbalanced nature of positive and negative sentiments the real time sentiment mining is a challenging machine learning task The main objective of this research work is to investigate the combined effect of machine learning classifiers and sampling methods in sentiment classification under imbalanced data distributions
Recently deep metric learning DML has achieved great success Some existing DML methods propose adaptive sample mining strategies which learn to weight the samples leading to interesting performance However these methods suffer from a small memory one training batch limiting their efficacy In this work we introduce a data driven method meta mining
Efficiently mining frequent itemsets is the key step in extracting association rules from large scale databases Considering the restriction of min support in mining association rules a weighted sampling algorithm for mining frequent itemsets is proposed in
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