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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2011

01.10.2011 | Original Article

An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network

verfasst von: Furao Shen, Hui Yu, Keisuke Sakurai, Osamu Hasegawa

Erschienen in: Neural Computing and Applications | Ausgabe 7/2011

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Abstract

An incremental online semi-supervised active learning algorithm, which is based on a self-organizing incremental neural network (SOINN), is proposed. This paper describes improvement of the two-layer SOINN to a single-layer SOINN to represent the topological structure of input data and to separate the generated nodes into different groups and subclusters. We then actively label some teacher nodes and use such teacher nodes to label all unlabeled nodes. The proposed method can learn from both labeled and unlabeled samples. It can query the labels of some important samples rather than selecting the labeled samples randomly. It requires neither prior knowledge, such as the number of nodes, nor the number of classes. It can automatically learn the number of nodes and teacher vectors required for a current task. Moreover, it can realize online incremental learning. Experiments using artificial data and real-world data show that the proposed method performs effectively and efficiently.
Vorheriger Artikel Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling
Nächster Artikel A non-biased form of least squares support vector classifier and its fast online learning

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Metadaten
Titel
An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network
verfasst von
Furao Shen
Hui Yu
Keisuke Sakurai
Osamu Hasegawa
Publikationsdatum
01.10.2011
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 7/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-010-0428-y

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