Dec 23, 2018 · In this paper, we proposed a model named sparse supervised representation classifier (SSRC) to solve the above-mentioned issues. The SSRC ...
In this paper, we proposed a model named sparse supervised representation classifier (SSRC) to solve the above-mentioned issues. The SSRC involves the class ...
Abstract— The sparse representation-based classification. (SRC) has been utilized in many applications and is an effective algorithm in machine learning.
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The proposed model named sparse supervised representation classifier (SSRC) can effectively classify uncontrolled and imbalanced data sets and a class ...
New algorithms are designed for imbalanced classification. Please read paper "Sparse Supervised Representation based Classifier for Uncontrolled and ...
According to this issue, this paper proposes an improved method based on sparse representation theory and over-sampling technique, named SROT (Sparse ...
Feb 23, 2023 · In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli ...
Sparse Supervised Representation-Based Classifier for Uncontrolled and Imbalanced. Classification: Shu et al. propose a model called Sparse Supervised ...
Feb 28, 2023 · SPISE iteratively resamples balanced subsets and combines the classifiers trained on these subsets for imbalanced classification. The diversity ...
Mar 7, 2024 · ... Sparse supervised representation-based classifier for uncontrolled and imbalanced classification. IEEE Trans Neural Netw Learn Syst 31(8): ...