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Construction costs forecasting: comparison of the accuracy of linear regression and support vector machine models

机译：建筑成本预测：线性回归准确性和支持向量机模型的比较

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1. Construction costs forecasting: comparison of the accuracy of linear regression and support vector machine models [J] . Petruseva Silvana, Zileska-Pancovska Valentina, ?ujo Vahida, Technical Gazette . 2017,第5期

机译：施工成本预测：线性回归和支持向量机模型精度的比较
2. Construction costs forecasting: comparison of the accuracy of linear regression and support vector machine models [J] . Petruseva Silvana, Zileska-Pancovska Valentina, ?ujo Vahida, Technical Gazette . 2017,第5期

机译：施工成本预测：线性回归和支持向量机模型精度的比较
3. Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine [J] . Gwang-Hee Kim, Jae-Min Shin, Sangyong Kim, Journal of Building Construction and Planning Research . 2013,第1期

机译：基于回归分析，神经网络和支持向量机的教学楼建筑成本估算方法的比较
4. Least Square Support Vector Machine Ensemble for Daily Rainfall Forecasting Based on Linear and Nonlinear Regression [C] . Jiansheng Wu, Mingzhe Liu, Long Jin Advances in neural network research and applications . 2010

机译：基于线性和非线性回归的最小二乘支持向量机集成的日降水量预测
5. Machine Learning: Several Advances in Linear Discriminant Analysis, Multi-View Regression and Support Vector Machine [D] . Zheng, Shuai 2017

机译：机器学习：线性判别分析，多视图回归和支持向量机的若干进展
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

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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

Construction costs forecasting: comparison of the accuracy of linear regression and support vector machine models

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