This paper presents a novel semi-supervised learning method which combines the power of learned similarity functions and classifiers. The approach capable of ...
This paper presents a novel semi-supervised learning method which combines the power of learned similarity functions and classifiers. The approach capable of ...
This paper presents a novel semi-supervised learning method which combines the power of learned similarity functions and classifiers. The approach capable of ...
In this paper, we introduced a new semi- supervised learning framework, SimMatch, which simulta- neously considers semantic similarity and instance similar- ity ...
It attempts to build a maximum-margin classifier on the data, while minimizing the corresponding inconsistency with the similarity matrix. This is achieved by ...
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Jun 12, 2024 · In this paper, we propose a novel method to take full advantage of the unlabeled data, termed DTS-SimL, which includes two core designs: Dual- ...
Dec 12, 2023 · Semi-supervised learning is a branch of machine learning that combines supervised and unsupervised learning by using both labeled and unlabeled data.
In semi-supervised boosting strategy, a similarity is needed to select unlabeled samples and then a pseudo label will be assigned to the unlabeled sample. A ...
Evis - Autonomous Traffic Monitoring by Embedded Vision · Monitoring System 100% · Traffic Monitoring 100% · Adaptation Mechanism 25% · Detection Method 25%.
In this paper, we consider the multiclass semi-supervised classification problem. A boosting algorithm is proposed to solve the multiclass problem directly.
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