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Enhanced kernel regression with prior knowledge in solving small sample problems

Shapiai, Mohd. Ibrahim and Sudin, Shahdan and Ibrahim, Zuwairie and Khalid, Marzuki (2011) Enhanced kernel regression with prior knowledge in solving small sample problems. In: 3rd International Conference On Computational Intelligence, Modelling And Simulation (CIMSIM 2011).

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Official URL: http://dx.doi.org/10.1109/CIMSim.2011.26

Abstract

In many real-world problems only very few samples are available and sometimes non-informative to help in performing a regression task. Incorporating a prior knowledge to this type of problem might offer a promising solution. In this study, the proposed algorithm translated a given prior knowledge and the available samples into a function space before introducing the idea of Pareto optimality concept to the problem. Instead of a single optimal solution competing with the objectives, the algorithm provides a set of solutions, generally denoted as the Pareto-optimal that offers more flexibility towards the intended solution. Thus the corresponding trade-off between solutions can be chosen in the presence of preference information. The proposed technique also does not require the addition of equality or non-equality constraints in introducing a prior knowledge. We also discussed, the challenges of determining the two objective functions that to be defined in the multi-objective problem environment. A benchmark function is used to validate the proposed technique, and it is shown that prior knowledge incorporation can relatively improve the regression performance.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:kernel regression
Divisions:Electrical Engineering
ID Code:45823
Deposited By: Haliza Zainal
Deposited On:10 Jun 2015 03:01
Last Modified:29 Aug 2017 03:53

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