Results 31 to 40 of about 338,614 (301)
Benchmarking the Semi-Supervised Naïve Bayes Classifier [PDF]
, 2015 Semi-supervised learning involves constructing predictive models with both labelled and unlabelled training data. The need for semi-supervised learning is driven by the fact that unlabelled data are often easy and cheap to obtain, whereas labelling data ...
Bagnall, Anthony +2 more
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Semi-Supervised Domain Adaptive Structure Learning
IEEE Transactions on Image Processing, 2022 Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. Unfortunately, a simple combination of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail to address such two objects because of ...
Can Qin +5 more
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Semi-supervised few-shot learning approach for plant diseases recognition
Plant Methods, 2021 Background Learning from a few samples to automatically recognize the plant leaf diseases is an attractive and promising study to protect the agricultural yield and quality.
Yang Li, Xuewei Chao
doaj +1 more source
Lautum Regularization for Semi-supervised Transfer Learning [PDF]
, 2020 Transfer learning is a very important tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a small number of training examples in the latter.
Giryes, Raja +2 more
core +1 more source
AlgorithmsPredicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the
Sami Kabir +2 more
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An Efficient Approach to Select Instances in Self-Training and Co-Training Semi-Supervised Methods
IEEE Access, 2022 Semi-supervised learning is a machine learning approach that integrates supervised and unsupervised learning mechanisms. In this learning, most of labels in the training set are unknown, while there is a small part of data that has known labels. The semi-
Karliane Medeiros Ovidio Vale +3 more
doaj +1 more source
Semi-Supervised Deep Learning for Fully Convolutional Networks
, 2017 Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training ...
D García-Lorenzo +4 more
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Semi-supervised Vocabulary-Informed Learning [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with ...
Fu, Yanwei, Sigal, Leonid
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Semi‐supervised learning dehazing algorithm based on the OSV model
IET Image Processing, 2023 Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging.
Lijun Zhu +5 more
doaj +1 more source
Fractional graph-based semi-supervised learning [PDF]
2017 25th European Signal Processing Conference (EUSIPCO), 2017 Publication in the conference proceedings of EUSIPCO, Kos island, Greece ...
de Nigris, Sarah +4 more
openaire +3 more sources