Combining MLC and SVM classifiers for learning based decision making: analysis and evaluations

Yi Zhang, Jinchang Ren, Jianmin Jiang

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
138 Downloads (Pure)


Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Accepted on May 11, 2015
Original languageEnglish
Article number423581
Number of pages8
JournalComputational Intelligence and Neuroscience
Publication statusPublished - 2015


  • maximum likelihood classifier
  • support vector machines
  • machine
  • decision making


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