Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis.
Neural networks (NNs), in general, and multi-layer perceptron (MLP), in particular, represent one of the most efficient classifiers among the machine learning (ML) algorithms. Inspired by the stimulus-sampling paradigm, it is plausible to assume that the association of stimuli with the neurons in th...
| Publicado en: | Journal of Biomedical Informatics Vol. 63; pp. 74 - 82 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Oct2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118967225&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118967225 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Oct2016 vid: 63 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 118967225 118967225 NLM27498068 118967225 10.1016/j.jbi.2016.08.004 NLM27498068 118967225 ppf: 74 ppct: 8 formats: tig: atl: Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis. aug: au: Gorunescu, Florin Belciug, Smaranda affil: Department of Biostatistics and Informatics, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania sug: subj: Algorithms Neural Networks (Computer) Diagnosis, Computer Assisted Human ab: Neural networks (NNs), in general, and multi-layer perceptron (MLP), in particular, represent one of the most efficient classifiers among the machine learning (ML) algorithms. Inspired by the stimulus-sampling paradigm, it is plausible to assume that the association of stimuli with the neurons in the output layer of a MLP can increase its performance. The stimulus-sampling process is assumed memoryless (Markovian), in the sense that the choice of a particular stimulus at a certain step, conditioned by the whole prior evolution of the learning process, depends only on the network's answer at the previous step. This paper proposes a novel learning technique, by enhancing the standard backpropagation algorithm performance with the aid of a stimulus-sampling procedure applied to the output neurons. The network uses the observable behavior that varies throughout the training process by stimulating the correct answers through corresponding rewards/penalties assigned to the output neurons. The proposed model has been applied in computer-aided medical diagnosis using five real-life breast cancer, colon cancer, diabetes, thyroid, and fetal heartbeat databases. The statistical comparison to well-established ML algorithms proved beyond doubt its efficiency and robustness. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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