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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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...
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...
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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Karol Grudziński (2010)
Control and Cybernetics
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Ireneusz Czarnowski, Piotr Jędrzejowicz (2011)
International Journal of Applied Mathematics and Computer Science
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The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning...
Roman Zajdel (2013)
International Journal of Applied Mathematics and Computer Science
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In this article, a new class of the epoch-incremental reinforcement learning algorithm is proposed. In the incremental mode, the fundamental TD(0) or TD(λ) algorithm is performed and an environment model is created. In the epoch mode, on the basis of the environment model, the distances of past-active states to the terminal state are computed. These distances and the reinforcement terminal state signal are used to improve the agent policy.
Francesc J. Ferri (1998)
Kybernetika
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Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbour (NN) classification rules both to improve its accuracy (editing) and to alleviate its computational burden (condensing). Methods based on selecting/discarding prototypes and methods based on adapting prototypes have been separately introduced to deal with this problem. Different approaches to this problem are considered in this paper and their main advantages and drawbacks are pointed out along...
Jasmina Novaković, Perica Strbac, Dusan Bulatović (2011)
The Yugoslav Journal of Operations Research
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María Dolores Castillo, José Ignacio Serrano (2005)
Mathware and Soft Computing
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The goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. The system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents. The learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. In classifying documents,...