A Piecewise Linear Network Classifier

Abdurrab, A.A.; Manry, M.T.; Li, Jiang; Malalur, Sanjeev S.; Gore, R.G.

doi:10.1109/ijcnn.2007.4371222

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…1) Piecewise Linear Classifier: Neural classifiers including the piecewise linear classifier (PLC) [9] are usually designed by minimizing the standard training error given by,…”

Section: B Feature/peak Selectionmentioning

confidence: 99%

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Prostate cancer biomarker identification using MALDI-MS DATA: Initial results

Mantena

Jiang

et al. 2009

2009 IEEE/NIH Life Science Systems and Applications Workshop

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We present a computational framework to identify prostate specific cancer biomarkers using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue imaging data collected at the Eastern Virginia Medical School (EVMS). Protein profiles of a tumor and its surrounding area from one prostate tissue sample were analyzed. The data contain 974 spectra (27 cancer, 947 normal). We proposed a pipeline to configure our previously developed feature selection and classification algorithms for biomarker identification. We also compared our algorithms with other popular computational models. Our feature selection algorithm identified three peaks (proteins) which obtained high sensitivities and specificities in a five-fold cross validation experiment.

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“…1) Piecewise Linear Classifier: Neural classifiers including the piecewise linear classifier (PLC) [9] are usually designed by minimizing the standard training error given by,…”

Section: B Feature/peak Selectionmentioning

confidence: 99%

“…The PLC approximates the general Bayes discriminant [9]. The available data are divided into a set of clusters where a local linear model is obtained for each cluster, by solving a set of linear equations.…”

Section: B Feature/peak Selectionmentioning

confidence: 99%

Prostate cancer biomarker identification using MALDI-MS DATA: Initial results

Mantena

Jiang

et al. 2009

2009 IEEE/NIH Life Science Systems and Applications Workshop

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show abstract

“…The PWL model has been proved to be able to approximate any continuous functions in a compact domain with arbitrary precision, 12,13 hence it can be used for dynamic system identification. PWL models are widely used in many fields, such as nonlinear circuits, 14 complex dynamic systems analysis and synthesis, 15 and neural networks 16‐18 . The adaptive hinging hyperplanes (AHH) model is one kind of PWL models, which is a universal approximator and has been shown effective in nonlinear system identification 19 .…”

Section: Introductionmentioning

confidence: 99%

Batch to batch optimal control based on multiinput multioutput adaptive hinging hyperplanes prediction and Kalman filter correction

Luo

Zhang

et al. 2020

Optim Control Appl Methods

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Summary A batch to batch optimal control strategy based on multiinput multioutput adaptive hinging hyperplanes (MIMO AHH) prediction and Kalman filter correction is proposed for the products quality control of the batch process. The model of AHH is one kind of piecewise linear models and is extended to the MIMO case in this article. The MIMO AHH is then used to develop the predictive model of the batch process. Due to the model‐plant mismatch and unknown disturbances, the optimal control policy calculated based on the MIMO AHH predictive model may not be optimal when applied to the true process. The Kalman filter is then utilized to correct the predictions of the current batch by considering the information of former batches. The effectiveness of the proposed strategy is verified through the simulation of a styrene batch polymerization reactor.

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The Turning Points on MLP’s Error Surface

Chen¹

Lecture Notes in Computer Science

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A Piecewise Linear Network Classifier

Cited by 4 publications

References 23 publications

Prostate cancer biomarker identification using MALDI-MS DATA: Initial results

Prostate cancer biomarker identification using MALDI-MS DATA: Initial results

Batch to batch optimal control based on multiinput multioutput adaptive hinging hyperplanes prediction and Kalman filter correction

The Turning Points on MLP’s Error Surface

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