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Comparing Performance of Interval Neutrosophic Sets and Neural Networks with Support Vector Machines for Binary Classification Problems

机译：区间中智集与神经网络与支持向量机的二进制分类问题的性能比较。

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Pawalai Kraipeerapun; Chun Che Fung;
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年度 2008
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1. Binary classification using ensemble neural networks and interval neutrosophic sets [J] . Pawalai Kraipeerapun, Chun Che Fung Neurocomputing . 2009,第13a15期

机译：使用集成神经网络和区间中智集进行二进制分类
2. Performance Evaluation Of Support Vector Machines (Svms)And Convolutional Neural Network (Cnn) On Binary Classification Problem [J] . Abdelaziz Botalb International Journal of Engineering Research and Applications . 2018,第9期

机译：支持向量机和卷积神经网络在二进制分类问题上的性能评估
3. Binary Color Classification For Brain Computer Interface Using Neural Networks And Support Vector Machines [J] . Charmi Sunil Mehta, Damini Ashok Chopra, Nidhi Milan Mavani, International Journal of Engineering Research and Applications . 2014,第4期

机译：使用神经网络和支持向量机的大脑计算机接口的二进制颜色分类
4. Comparing Performance of Interval Neutrosophic Sets and Neural Networks with Support Vector Machines for Binary Classification Problems [C] . Pawalai Kraipeerapun, Chun Che Fung IEEE International Conference on Digital Ecosystems and Technologies . 2008

机译：间隔中使用子系统的性能和神经网络对二进制分类问题的支持
5. Vertical equal-interval neighborhood ring-based k-nearest neighbor/local support vector machine classification and applications. [D] . Pan, Fei. 2004

机译：基于垂直等间隔邻域环的k最近邻/局部支持向量机的分类和应用。
6. EEG signal analysis using classification techniques: Logistic regression artificial neural networks support vector machines and convolutional neural networks [O] . Maria Camila Guerrero, Juan Sebastián Parada, Helbert Eduardo Espitia 2021

机译：EEG信号分析使用分类技术：Logistic回归人工神经网络支持向量机和卷积神经网络
7. Comparing performance of interval neutrosophic sets and neural networks with support vector machines for binary classification problems [O] . Kraipeerapun, P., Fung, C.C. 2008

机译：区间中智集和神经网络与支持向量机的性能比较

Comparing Performance of Interval Neutrosophic Sets and Neural Networks with Support Vector Machines for Binary Classification Problems

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