I am working on a decision making system, something about concert prices prediction to maximize the profit. Because it is multi-output, now data mining algorithm I know only neural network is suitable for multi-output, but the result is different every time. So I want to try SVM, however, SVM is only suit for single output, so I want to improve SVM with multi-output, but I don't know is it feasible? BTW, these outputs has relationship, I can't output these independently by running SVM several times.
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possible duplicate of [Best way to perform multiclass SVM](http://stats.stackexchange.com/questions/21465/best-way-to-perform-multiclass-svm) – gung - Reinstate Monica Dec 25 '14 at 03:46
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Actually, I use the SVR for regression, so it is not a multi-class problem, it is a multi-output regression problem. – Wendy LEE Dec 25 '14 at 03:52
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In the sense of multiple different continuous response variables, or in the sense that the single continuous response variable can take multiple values? – gung - Reinstate Monica Dec 25 '14 at 03:55
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For example, the concert tickets have several prices, this model is to predict the related price, so it has several prices. maybe 5 or 6 output – Wendy LEE Dec 25 '14 at 04:02
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OK, maybe that's different enough. I retracted my close vote. We can see what the SVM experts think. – gung - Reinstate Monica Dec 25 '14 at 04:05
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THX, GongSun!! 谢谢！！ – Wendy LEE Dec 25 '14 at 10:55