Journal ArticleDOI
On incrementally using a small portion of strong unlabeled data for semi-supervised learning algorithms
Thanh-Binh Le,Sang-Woon Kim +1 more
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TLDR
Experimental results demonstrate that the simply recycled selection and incrementally reinforced selection strategies, i.e., selecting only a small portion of strong examples from the available unlabeled data in an incremental fashion, can compensate for the shortcomings of the existing SSMB algorithm.About:
This article is published in Pattern Recognition Letters.The article was published on 2014-05-01. It has received 12 citations till now. The article focuses on the topics: Semi-supervised learning.read more
Citations
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Journal ArticleDOI
Semi-supervised classification via kernel low-rank representation graph
TL;DR: The results show that the proposed SKLRG can achieve better performance than its counterparts when there are only a small number of labeled samples, and can capture the global structure of complex data and implements more robust subspace segmentation.
Journal ArticleDOI
An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets
Ana Stanescu,Doina Caragea +1 more
TL;DR: In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions.
Journal ArticleDOI
Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines
Thanh-Binh Le,Sang-Woon Kim +1 more
TL;DR: The proposed algorithm can compensate for the shortcomings of the traditional S3VMs and, when compared with previous approaches, can achieve further improved results in terms of the classification accuracy.
Proceedings ArticleDOI
Achieving semi-supervised incremental learning with Learn++ and simple recycled selection
TL;DR: An incremental semi-supervised learning method called SSLearn++ is proposed, which is based on the techniques Simple Recycled Selection (SRS), and Learn++ (incremental learning), and results show the proposed method is promising.
Journal ArticleDOI
Virtual sample generation for few-shot source camera identification
TL;DR: In this article , a semi-supervised, Mega-Trent-Diffusion (MTD) method is proposed to generate virtual samples, such that the training sets can be expanded and unlabeled samples can be fully utilized as well.
References
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Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Proceedings ArticleDOI
Combining labeled and unlabeled data with co-training
Avrim Blum,Tom M. Mitchell +1 more
TL;DR: A PAC-style analysis is provided for a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views, to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples.
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