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Full-Text Articles in Physical Sciences and Mathematics
Multiview Semi-Supervised Learning With Consensus, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi
Multiview Semi-Supervised Learning With Consensus, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications. Semi-supervised learning aims to improve the performance of a classifier trained with limited number of labeled data by utilizing the unlabeled ones. This paper demonstrates a way to improve the transductive SVM, which is an existing semi-supervised learning algorithm, by employing a multiview learning paradigm. Multiview learning is based on the fact that for some problems, there may exist multiple perspectives, so called views, of each data sample. For example, in text classification, the typical view contains a large number of raw content features such …
Using Monte Carlo Tree Search For Replanning In A Multistage Simultaneous Game, Daniel Beard, Philip Hingston, Martin Masek
Using Monte Carlo Tree Search For Replanning In A Multistage Simultaneous Game, Daniel Beard, Philip Hingston, Martin Masek
Research outputs 2012
In this study, we introduce MC-TSAR, a Monte Carlo Tree Search algorithm for strategy selection in simultaneous multistage games. We evaluate the algorithm using a battle planning scenario in which replanning is possible. We show that the algorithm can be used to select a strategy that approximates a Nash equilibrium strategy, taking into account the possibility of switching strategies part way through the execution of the scenario in the light of new information on the progress of the battle.
A Multimodal Problem For Competitive Coevolution, Philip Hingston, Tirtha Ranjeet, Chiou Peng Lam, Martin Masek
A Multimodal Problem For Competitive Coevolution, Philip Hingston, Tirtha Ranjeet, Chiou Peng Lam, Martin Masek
Research outputs 2012
Coevolutionary algorithms are a special kind of evolutionary algorithm with advantages in solving certain specific kinds of problems. In particular, competitive coevolutionary algorithms can be used to study problems in which two sides compete against each other and must choose a suitable strategy. Often these problems are multimodal - there is more than one strong strategy for each side. In this paper, we introduce a scalable multimodal test problem for competitive coevolution, and use it to investigate the effectiveness of some common coevolutionary algorithm enhancement techniques.