why using support vector machine?

Question

I have some questions about SVM : 1- Why using SVM? or in other words, what causes it to appear? 2- The state Of art (2017) 3- What improvements have they made?

Answer 1

SVM works very well. In many applications, they are still among the best performing algorithms.

We've seen some progress in particular on linear SVMs, that can be trained much faster than kernel SVMs.

Read more literature. Don't expect an exhaustive answer in this QA format. Show more effort on your behalf.

Answer 2

SVM's are most commonly used for classification problems where labeled data is available (supervised learning) and are useful for modeling with limited data. For problems with unlabeled data (unsupervised learning), then support vector clustering is an algorithm commonly employed. SVM tends to perform better on binary classification problems since the decision boundaries will not overlap. Your 2nd and 3rd questions are very ambiguous (and need lots of work!), but I'll suffice it to say that SVM's have found wide range applicability to medical data science. Here's a link to explore more about this: Applications of Support Vector Machine (SVM) Learning in Cancer Genomics