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https://pubsonline.informs.org/doi/10.1287/mksc.1050.0123
Nov 01, 2005 · Many marketing problems require accurately predicting the outcome of a process or the future state of a system. In this paper, we investigate the ability of the support vector machine to predict outcomes in emerging environments in marketing, such as automated modeling, mass-produced models, intelligent software agents, and data mining.Cited by: 167
https://pubsonline.informs.org/doi/abs/10.1287/mksc.1050.0123
Many marketing problems require accurately predicting the outcome of a process or the future state of a system. In this paper, we investigate the ability of the support vector machine to predict outcomes in emerging environments in marketing, such as automated modeling, mass-produced models, intelligent software agents, and data mining.Cited by: 167
http://ijctet.org/assets/upload/2298ijctet2016020202.pdf
Stock Market Prediction Using Support Vector Machine Mr. Sachin Sampat Patil, Prof. Kailash Patidar, Assistant Prof. Megha Jain SSSIST, Sehore, Madhya Pradesh, India Abstract—a lot of studies provide strong evidence that traditional predictive regression models face significant challenges in …
https://www.researchgate.net/post/How_support_vector_machine_is_used_in_prediction
How support vector machine is used in prediction? ... I have read some articles which discuss about using support vector machine in predicting the cost, but when I took a look on Wikipedia ...
https://www.cs.princeton.edu/sites/default/files/uploads/saahil_madge.pdf
prediction using various machine learning models, and found that the best results were achieved with SVM[15]. His prediction rate of 60% agrees with Kim’s conclusion. Since most recent research has incorporated SVMs, this is the technique we use in our analysis. 2.4.Support Vector MachinesCited by: 8
https://www.sciencedirect.com/science/article/pii/S0169743903001114
Nov 28, 2003 · The goal of this paper is to use a support vector machine (SVM) for the task of time series prediction. SVM is a relatively new nonlinear technique in the field of chemometrics and it has been shown to perform well for classification tasks , regression and time series prediction .Cited by: 427
http://ijmtst.com/NCCIT2017/4NCCIT04.pdf
1.4.1 Support Vector Machine SVMs are a popular machine learning method for classification, regression, & other learning tasks. LIBSVM is a library for Support Vector Machines (SVMs). A typical use of LIBSVM involves two steps: first, training a data set to obtain a model & d, using the model to predict information of a testing data set.
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