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Semi-supervised Ant Evolutionary Classification
2014In this paper, we propose an ant evolutionary classification model, which treats different classes as ant colonies to classify the unlabeled instances. In our model, each ant colony sends its members to propagate its unique pheromone on the unlabeled instances. The unlabeled instances are treated as unlabeled ants.
Ping He +5 more
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Enhanced manifold regularization for semi-supervised classification
Journal of the Optical Society of America A, 2016Manifold regularization (MR) has become one of the most widely used approaches in the semi-supervised learning field. It has shown superiority by exploiting the local manifold structure of both labeled and unlabeled data. The manifold structure is modeled by constructing a Laplacian graph and then incorporated in learning through a smoothness ...
Haitao, Gan +3 more
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Semi-supervised learning for image classification
2012Objektklassifizierung ist ein aktives Forschungsgebiet in maschineller Bildverarbeitung was bisher nur unzureichend gelöst ist. Die meisten Ansätze versuchen die Aufgabe durch überwachtes Lernen zu lösen. Aber diese Algorithmen benötigen eine hohe Anzahl von Trainingsdaten um gut zu funktionieren. Das führt häufig entweder zu sehr kleinen Datensätzen (<
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Semi-Supervised Classification via Local Spline Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010This paper presents local spline regression for semi-supervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the data points directly to be class labels. The spline is composed of polynomials and Green's functions.
Shiming, Xiang +2 more
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Semi-Supervised Text Classification With Universum Learning
IEEE Transactions on Cybernetics, 2016Universum, a collection of nonexamples that do not belong to any class of interest, has become a new research topic in machine learning. This paper devises a semi-supervised learning with Universum algorithm based on boosting technique, and focuses on situations where only a few labeled examples are available.
Chien-Liang, Liu +4 more
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Semi-supervised Classification and Noise Detection
2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009Semi-supervised learning has become a topic of significant interests recently. In this paper, we are concerned with semi-supervised classification and noise detection. Based on label propagation algorithm, we present an improved label propagation algorithm, which can classify data and detect noise simultaneously.
Yunna Duan +4 more
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Semi-supervised classification with cluster ensemble
2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), 2017We propose a method for semi-supervised classification using a combination of ensemble clustering and kernel based learning. The method works in two steps. In the first step, a number of variants of clustering partition are obtained with some clustering algorithm working on both labeled and unlabeled data.
Vladimir Berikov +2 more
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Semi-supervised classification with pairwise constraints
Neurocomputing, 2014Graph-based semi-supervised learning has been intensively investigated for a long history. However, existing algorithms only utilize the similarity information between examples for graph construction, so their discriminative ability is rather limited.
Chen Gong +4 more
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Phenotype Prediction with Semi-supervised Classification Trees
2018In this work, we address the task of phenotypic traits prediction using methods for semi-supervised learning. More specifically, we propose to use supervised and semi-supervised classification trees as well as supervised and semi-supervised random forests of classification trees. We consider 114 datasets for different phenotypic traits referring to 997
Levatić, Jurica +7 more
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