V
Vojislav Kecman
Researcher at Virginia Commonwealth University
Publications - 101
Citations - 3468
Vojislav Kecman is an academic researcher from Virginia Commonwealth University. The author has contributed to research in topics: Support vector machine & Artificial neural network. The author has an hindex of 23, co-authored 101 publications receiving 3252 citations. Previous affiliations of Vojislav Kecman include University of Auckland & Korea University.
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Book
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
TL;DR: This textbook provides a thorough introduction to the field of learning from experimental data and soft computing and assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole.
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New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks
TL;DR: In this article, a new approach to modelling dynamic processes of vapour-compression liquid refrigeration systems using a dynamic neural network model for the performance prediction has been proposed using a generalised radial basis function neural network as inputs require only those parameters that are easily measurable.
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Multi-Target Support Vector Regression Via Correlation Regressor Chains
TL;DR: The results show that the maximum correlation SVR approach improves the performance of using ensembles of random chains, which is used to build a single chained support vector regression model, improving the models’ prediction performance while reducing the computational complexity.
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Multiple regression and neural networks analyses in composites machining
TL;DR: In this paper, the machining forces-tool wear relationship of an aluminium metal matrix composite has been studied using multiple regression analysis (MRA) and generalised radial basis function (GRBF) neural network.