Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5433
Title: An efficient algorithm for large-scale quasi-supervised learning
Authors: Karaçalı, Bilge
Keywords: Large-scale pattern recognition
Nearest neighbor rule
Posterior probability estimation
Quasi-supervised learning
Transductive inference
Issue Date: May-2016
Publisher: Springer Verlag
Source: Karaçalı, B. (2016). An efficient algorithm for large-scale quasi-supervised learning. Pattern Analysis and Applications, 19(2), 311-323. doi:10.1007/s10044-014-0401-y
Abstract: We present a novel formulation for quasi-supervised learning that extends the learning paradigm to large datasets. Quasi-supervised learning computes the posterior probabilities of overlapping datasets at each sample and labels those that are highly specific to their respective datasets. The proposed formulation partitions the data into sample groups to compute the dataset posterior probabilities in a smaller computational complexity. In experiments on synthetic as well as real datasets, the proposed algorithm attained significant reduction in the computation time for similar recognition performances compared to the original algorithm, effectively generalizing the quasi-supervised learning paradigm to applications characterized by very large datasets.
URI: http://doi.org/10.1007/s10044-014-0401-y
http://hdl.handle.net/11147/5433
ISSN: 1433-7541
1433-755X
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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