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Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

机译：分类算法基本原则K-最近邻，支持向量机，随机森林和神经网络：综述

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Ernest Yeboah Boateng; Joseph Otoo; Daniel A. Abaye;
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1. Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review [J] . Ernest Yeboah Boateng, Joseph Otoo, Daniel A. Abaye Journal of Data Analysis and Information Processing . 2020,第04期

机译：分类算法基本原则K-最近邻，支持向量机，随机森林和神经网络：综述
2. Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review [J] . Ernest Yeboah Boateng, Joseph Otoo, Daniel A. Abaye 数据分析和信息处理（英文） . 2020,第004期

机译：分类算法K最近邻，支持向量机，随机森林和神经网络的基本原理：综述
3. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests [J] . Jo?o Maroco, Dina Silva, Ana Rodrigues, BMC research notes . 2011,第1期

机译：痴呆症预测中的数据挖掘方法：线性判别分析，逻辑回归，神经网络，支持向量机，分类树和随机森林的准确性，敏感性和特异性的真实数据比较
4. Comparison of Multinomial Naïve Bayes with K-Nearest Neighbors, Support Vector Machine and Random Forest for Classification of “Network Attacks” Document [C] . Bambang Harjito, Ardhi Wijayanto, Kuni Nur Aini, International Conference on Informatics and Computing . 2019

机译：多项式朴素贝叶斯与K最近邻，支持向量机和随机森林的比较，用于“网络攻击”文档的分类
5. Comparative classification of prostate cancer data using the Support Vector Machine, Random Forest, DualKS and k-Nearest Neighbours. [D] . Sakouvogui, Kekoura. 2015

机译：使用支持向量机，Random Forest，DualKS和k-Nearest邻居对前列腺癌数据进行比较分类。
6. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy sensitivity and specificity of linear discriminant analysis logistic regression neural networks support vector machines classification trees and random forests [O] . João Maroco, Dina Silva, Ana Rodrigues, 2011

机译：痴呆症预测中的数据挖掘方法：线性判别分析逻辑回归神经网络支持向量机分类树和随机森林的准确性敏感性和特异性的真实数据比较
7. Data mining methods in the prediction of dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests [O] . Maroco, João, Silva, Dina, Rodrigues, Ana, 2011

机译：痴呆症预测中的数据挖掘方法：线性判别分析，逻辑回归，神经网络，支持向量机，分类树和随机森林的准确性，敏感性和特异性的真实数据比较

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

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