A Neural Network Approach for Online Nonlinear Neyman-Pearson Classification

We propose a novel Neyman-Pearson (NP) classifier that is both online and nonlinear as the first time in the literature. The proposed classifier operates on a binary labeled data stream in an online manner, and maximizes the detection power about a user-specified and controllable false positive rate...

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Veröffentlicht in:	IEEE access 2020, Vol.8, p.210234-210250
Hauptverfasser:	Can, Basarbatu, Ozkan, Huseyin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Classification Classifiers Complexity Complexity theory Computational modeling Controllability Data transmission Kernel large scale neural network Neural networks Neyman-Pearson nonlinear online Optimization Radial basis function Support vector machines Tuning
Online-Zugang:	Volltext
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