How to learn SVM model with one input?











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How I can train Accord.NET MulticlassSupportVectorLearning<Linear> with only one input? My input data contains 10 attributes (all string values converted to double by Bag of words) and can result in one of the 20+ classes. I train it once with available data 100+ and all works fine. Then I get data one by one from the user and I need to "improve" my model by this one input every time it comes. Is it possible?






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    How I can train Accord.NET MulticlassSupportVectorLearning<Linear> with only one input? My input data contains 10 attributes (all string values converted to double by Bag of words) and can result in one of the 20+ classes. I train it once with available data 100+ and all works fine. Then I get data one by one from the user and I need to "improve" my model by this one input every time it comes. Is it possible?






    c# svm accord.net







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      How I can train Accord.NET MulticlassSupportVectorLearning<Linear> with only one input? My input data contains 10 attributes (all string values converted to double by Bag of words) and can result in one of the 20+ classes. I train it once with available data 100+ and all works fine. Then I get data one by one from the user and I need to "improve" my model by this one input every time it comes. Is it possible?






      c# svm accord.net







      share|improve this question









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      Patrik Křemeček is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      How I can train Accord.NET MulticlassSupportVectorLearning<Linear> with only one input? My input data contains 10 attributes (all string values converted to double by Bag of words) and can result in one of the 20+ classes. I train it once with available data 100+ and all works fine. Then I get data one by one from the user and I need to "improve" my model by this one input every time it comes. Is it possible?






      c# svm accord.net




      c# svm accord.net






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      asked Nov 4 at 9:55









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