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https://arxiv.org/pdf/1005.1545.pdf
Semi-supervised support vector machines (S3VMs) are a kindof popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been found helpful in many situations, they may degenerate performance and the …
https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/aaai11s3vmus.pdf
Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection ⁄ Yu-Feng Li Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210093, China Abstract Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning
https://ui.adsabs.harvard.edu/abs/2010arXiv1005.1545L/abstract
Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been found helpful in many situations, they may degenerate performance and the resultant generalization ability may be even worse than using the labeled data only.Author: Yu-Feng Li, Zhi-Hua Zhou
https://www.researchgate.net/publication/45916665_Improving_Semi-Supervised_Support_Vector_Machines_Through_UnlabeledInstances_Selection
Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. …
https://arxiv.org/abs/1005.1545
Abstract: Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been found helpful in many situations, they may degenerate performance and the resultant generalization ability may be even worse than using the labeled data only.Author: Yu-Feng Li, Zhi-Hua Zhou
https://www.sciencedirect.com/science/article/pii/S0167865515001282
Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines. Author links open overlay ... unlabeled data for improving semi-supervised support vector ... H. ZhouImproving semi-supervised support vector machines through unlabeled instances selection. Proceedings of the 25th AAAI Conference on ...Cited by: 4
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.4462
Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been found helpful in many situations, they may de-generate performance and the resultant generalization ability may be even worse than using the labeled data only.
https://archive.org/details/arxiv-1005.1545
Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though...
https://link.springer.com/article/10.1007%2Fs11704-019-8452-2
Jun 18, 2019 · Li Y F, Zhou Z H. Improving semi-supervised support vector machines through unlabeled instances selection. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011, 386–391 Google Scholar
https://link.springer.com/article/10.1007%2Fs11704-017-6543-5
Li Y F, Zhou Z H. Improving semi-supervised support vector machines through unlabeled instances selection. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011, 386–391 Google Scholar
https://dl.acm.org/citation.cfm?id=1390688
Jun 01, 2008 · Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S 3 VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem.
http://journal.hep.com.cn/fcs/EN/abstract/abstract19476.shtml
Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affect performance when using unlabeled data. In this paper, we propose a new graph-based semi-supervised learning method based on instance selection in order to reduce the chances of performance degeneration.
https://core.ac.uk/display/2122400
Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection . By Yu-Feng Li and Zhi-Hua Zhou. Abstract. Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been found helpful in many situations, they ...
https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/publication_toc.htm
Improving semi-supervised support vector machines through unlabeled instances selection. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA, 2011, pp.386-391. (CORR abs/1005.1545)
http://scholar.google.com/citations?user=5iQDMtUAAAAJ&hl=en
This "Cited by" count includes citations to the following articles in Scholar. ... Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection. YF Li, ZH Zhou. Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011. 53: 2011:
https://www.sciencedirect.com/science/article/pii/S0952197619300302
Li Y.-F., Zhou Z.-H.Improving semi-supervised support vector machines through unlabeled instances selection Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI Press (2011), pp. 500-505
https://www.researchgate.net/publication/220696326_Introduction_to_Semi-Supervised_Learning
Introduction to Semi-Supervised Learning. ... function over-fitting on seen unlabeled instances due to lack of extrapolation power which makes graph Laplacian regularization based solution biased ...
http://journal.hep.com.cn/fcs/EN/10.1007/s11704-019-8452-2
Improving semi-supervised support vector machines through unlabeled instances selection. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011, 386–391 25
https://www.ijert.org/a-comprehensive-study-and-analysis-of-semi-supervised-learning-techniques
for classification of labeled data. We can modify supervised SVMs to Semi Supervised Support Vector Machines (SSSVM OR S3VM) by using labeled data in a Hilbert space by extending vector algebra tools and calculus from a two-dimensional Euclidean plane and three-dimensional space to spaces with a myriad number of dimensions.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3806632/
Few studies have examined the impact of unlabeled data on semi-supervised classifier performance. For this dataset, Laplacian SVM performance was significantly positively correlated with the number of unlabeled instances used for training (Figure 4). Using few (<1000) unlabeled instances did not improve performance relative to the supervised SVM.
https://content.iospress.com/articles/intelligent-data-analysis/ida184195
Active learning is essentially a supervised learning procedure by selecting the most informative unlabeled instances through an uncertainty ... high confidence in the form of soft class labels during the supervised learning procedure on improving the generalization performance. ... semi-supervised support vector machines using label mean for ...
https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169689
2 Semi-supervised regression. In many real world applications, there is often a multitude of unlabeled data, while labeled data is scarce. Labeling unlabeled data is strenuous, timewasting, and it is too expensive as it requires specialists, real time experiments, …
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