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Integration of Bayesian Classifier and Perceptron for Problem Identification on Dynamics Signature Using a Genetic Algorithm for the Identification Threshold Selection

机译：贝叶斯分类器和感知器的集成，用于使用遗传算法识别动态阈值的动态签名问题识别

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An approach to the integration of multiple methods of user authentication and example of multi-classifier Bayesian and neural network is presented. The approach offers to find the convolution of outputs from multiple classifiers based on the complementary functions and to carry out the selection of the identification thresholds for each of the users. A number of complementary functions that use fundamentally different mathematical functions is analyzed. It is shown the practical need in metaheuristic algorithms for selecting the identification thresholds by comparison with the classic gradient method. The effectiveness some of the proposed series of multi-function, compared with the single use Bayes classifier and neural network is showed.

机译：提出了一种集成多种用户认证方法的方法，并给出了多分类器贝叶斯网络和神经网络的实例。该方法提供了基于互补功能找到来自多个分类器的输出的卷积，并为每个用户执行了识别阈值的选择。分析了一些使用根本不同的数学函数的互补函数。通过与经典梯度法比较，显示出在元启发式算法中选择识别阈值的实际需求。与单次使用贝叶斯分类器和神经网络相比，本文提出的一系列多功能产品的有效性得到了证明。

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《International symposium on neural networks》|2016年|620-627|共8页
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作者
Evgeny Kostyuchenko; Mihail Gurakov; Egor Krivonosov; Maxim Tomyshev; Roman Mescheryakov; Ilya Hodashinskiy;
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关键词
Signature identification; Bayesian; Neural network; Complex method; Identification threshold;

机译：签名识别;贝叶斯神经网络;复杂的方法;鉴定阈值;

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Integration of Bayesian Classifier and Perceptron for Problem Identification on Dynamics Signature Using a Genetic Algorithm for the Identification Threshold Selection

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