SVMs can be used to solve various real world problems:
• SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
• Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
• SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
• Hand-written characters can be recognized using SVM
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