Support Vector Machine (SVM) and logistic regression (LR) have been discussed widely in machine learning community, I know that both of them achieve pretty good performance. But, I am not sure how in general SVM compared to logistic regression? Why sometime SVM can perform better than LR? And sometime not? What factors determine these.
LR can only be compared to linear SVM - both work in the data space. I dont know exactly when logistic regression will perform better than linear SVM, but you should not that they approach the problem from a different angle - logistic regression will look at all the data in both classes while a linear SVM will only take into consideration the data in the border between both classes.
A RBF or polynomial SVM will (in my limited experience) perform better than a LR in most cases. There was a very recent paper on the JMLR http://jmlr.org/papers/v15/delgado14a.html that reached that same conclusion. They also probably tested LR and linear SVM on a large set of datasets, so you can compare if linear SVM has a similar performance to LR, for practical purposes.
This answer assumes that you are talking about a classification problem, despite the fact that you included a tag on regression.