Multiple-instance learning with pairwise instance similarity
Liming Yuan, Jiafeng Liu, Xianglong Tang (2014)
International Journal of Applied Mathematics and Computer Science
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Liming Yuan, Jiafeng Liu, Xianglong Tang (2014)
International Journal of Applied Mathematics and Computer Science
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Urszula Boryczka (2010)
Control and Cybernetics
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Zengyou He, Xiaofei Xu, Shenchun Deng (2006)
Computer Science and Information Systems
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Goran Martinović, Dražen Bajer, Bruno Zorić (2014)
International Journal of Applied Mathematics and Computer Science
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Goran Martinović, Dražen Bajer, Bruno Zorić (2014)
International Journal of Applied Mathematics and Computer Science
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Zou, Wenping, Zhu, Yunlong, Chen, Hanning, Sui, Xin (2010)
Discrete Dynamics in Nature and Society
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Michał Woźniak, Bartosz Krawczyk (2012)
International Journal of Applied Mathematics and Computer Science
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This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier,...
Piotr Kulczycki, Szymon Łukasik (2014)
International Journal of Applied Mathematics and Computer Science
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Liming Yuan, Jiafeng Liu, Xianglong Tang (2014)
International Journal of Applied Mathematics and Computer Science
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Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency...
Zengyou He, Xiaofei Xu, Joshua Zhexue Huang, Shengchun Deng (2005)
Computer Science and Information Systems
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Koychev, Ivan (2007)
Serdica Journal of Computing
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This paper addresses the task of learning classifiers from streams of labelled data. In this case we can face the problem that the underlying concepts can change over time. The paper studies two mechanisms developed for dealing with changing concepts. Both are based on the time window idea. The first one forgets gradually, by assigning to the examples weight that gradually decreases over time. The second one uses a statistical test to detect changes in concept and then optimizes the...
Marcin Gorawski, Tadeusz Morzy, Robert Wrembel (2009)
Control and Cybernetics
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