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https://www.researchgate.net/publication/232614902_An_Uncertainty_Sampling-Based_Active_Learning_Approach_for_Support_Vector_Machines
To reduce the amount of human labeling effort while maintaining the SVMs performance, in this work we propose an uncertainty sampling-based active learning approach for SVMs to annotate the most ...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861916/
Methods. We integrated an uncertainty sampling AL approach with support vector machines-based phenotyping algorithms and evaluated its performance using three annotated disease cohorts including rheumatoid arthritis (RA), colorectal cancer (CRC), and venous thromboembolism (VTE).Cited by: 85
http://mypages.iit.edu/~msharm11/publications/sharma_dmkd2017.pdf
Evidence-Based Uncertainty Sampling for Active Learning 3 { We empirically evaluated our methods on several real-world datasets and showed that distinguishing between the reasons for uncertainty is useful to improve active learning. { We provided formulation of evidence for na ve Bayes, logistic regression, and support vector ma-
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144157/
The experiments are conducted to compare the proposed active learning approach to a number of active learning methods, including (1) BvSB , which is the uncertainty sampling method, and (2) information density (ID), which denotes the active learning method in that uses the cosine distance to measure an information density and selects uncertain ...Cited by: 5
http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005376609
Support vector machines (SVMs) have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, SVMs is a supervised learning which is based on the assumption that it is straightforward to obtain labeled data, but in reality labeled data can be scarce or expensive to obtain.
https://www.sciencedirect.com/science/article/pii/S0925231219309610
Usually an uncertainty sampling active learning algorithm is associated with a classifier, which is used to evaluate the uncertainty of each instance in the active pool. ... The proposed approach is based on the ECOC framework, we select the instances that the ECOC is most uncertain about. ... active learning with support vector machines ...Author: Shilin Gu, Yang Cai, Jincheng Shan, Chenping Hou
https://www.sciencedirect.com/science/article/pii/S0031320312001550
In this paper, a new pool-based active learning strategy called inconsistency-based active learning (I-AL), as well as a specific algorithm called inconsistency-based active learning for SVM (I-ALSVM), which uses the inconsistency value of unlabeled example as the selection criterion, is proposed.Cited by: 20
https://rdrr.io/github/ramhiser/activelearning/man/uncertainty_sampling.html
The 'uncertainty sampling' approach to active learning determines the unlabeled observation which the user-specified supervised classifier is "least certain." The "least certain" observation should then be queried by the oracle in the "active learning" framework.Author: John A. Ramey
https://www.researchgate.net/publication/225138938_Representative_Sampling_for_Text_Classification_Using_Support_Vector_Machines
Representative Sampling for Text Classification Using Support Vector Machines. ... vs. uncertainty sampling for active learning—Unlabeled points selected by representative sampling are the ...
https://arxiv.org/pdf/1702.08540.pdf
Active Learning Using Uncertainty Information Yazhou Yangy, Marco Loogz ... certainty sampling approach preferred the instances with max- ... to the current learning boundary using the classifier of support vector machines. Campbell et al. [7] shared the same idea with Tong and Koller [6].
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