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All movies about the joint feature or allude to rape as something routine.
Previous works have shown that the joint feature distribution of two properties can improve the performance.
In conventional approaches, the joint feature and the surface feature are discussed separately.
We propose a mixed negative instance sampling strategy to learn the weights of different joint feature representations.
We propose a deep boosting framework based on layer-by-layer joint feature boosting and dictionary learning.
In this paper, we introduce l1-norm driven sparse representation into feature selection, and propose a novel joint feature weights learning algorithm, named sparse discriminative feature weights (SDFW).
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The model based on sequential joint features of Aac, Sse and Acc outperformed any other pure sequential features-based model (Table 1).
Specifically, we first introduce a joint feature-sample selection (JFSS) method for selecting an optimal subset of samples and features, to learn a reliable diagnosis model.
Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data.
Four realistic joint features were then modelled by this validated modelling approach.
Finite Element Analysis is used in conjunction with Cohesive Zone Modelling (CZM) to predict the strength of selected joint features.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com