How could we use SVMs step by step,
the labeling process for SVMs is generated by experts, or automatically.
Hi Mohammad,
Support Vector Machines (SVM) are a class of supervised learning algorithms often used for classification or sometimes regression tasks. Generally, you first need to build an SVM to either classify a set of samples into their respective classes (SVM for classification) or fit a set of data samples to an expected target (SVM for regression). In either of the above case, you need to use part of the entire data to train the built model and used the remaining data to test the trained model. As for the label, in the case of supervised learning (for classification problems), the labels should be decided by an expert.
Kindly see the following article for SVM application: "Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis"
Good luck...
Hi Mohammad,
in addition to the comments above, you can also have a look at the ARDUOUS workshop series. They address the labelling of data needed for classification tasks. I think you can find a lot of useful materials there.
https://www.researchgate.net/project/ARDUOUS-International-Workshop-on-Annotation-of-useR-Data-for-UbiquitOUs-Systems
https://text2hbm.org/arduous/
Dear Mohammed;
I think, it is useful to see the following tutorial :
https://link.springer.com/content/pdf/10.1023%2FB%3ASTCO.0000035301.49549.88.pdf
Regards
Hi Oluwarotimi,
The data in the label (class) also should generated by expert?
(ex. label fruit generated by expert to clasiify food, the foods which belong to fruit label such as apple and orange also genrated by the expert.)
Dear Mohammad Khalaf
I think these two URL will help you to understand SVM and explain how to use it for classification:
https://www.csie.ntu.edu.tw/~cjlin/libsvm/
http://www.vlfeat.org/overview/svm.html#tut.svm
Best wishes
I. Omara
Dear Mohammad Nooman Khalaf
This paper can help to understand that
Article Advanced Meta-Heuristics, Convolutional Neural Networks, and...
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