title: What's the different of "classify" between softmax, logistic and svm?
body: <p>I'm using caffe to do the object detection with SSD model, and recently work I adjust the loss type of "MultiBoxLoss".
  In the multibox_loss_layer.cpp file, its loss has SOFTMAX as default and LOGISTIC option, I add the hingeloss(SVM) option into caffe code, and do the training but the result is bad.</p>
  
  <p>Now the boss want me to use SVM to classify the feature map by python sklearn.</p>
  
  <p>And a question come across to me, in the multibox_loss_layer.cpp file, there can use the softmax, logistic and hingeloss to calculate the loss. On this step, its data is just "one-dimension", but the feature map is high-dimension, and I internet the article, it seem softmax can't classify high-dimension data.</p>
  
  <p>Ex: if there have three class: cat, dog and rabbit, then it's one-dimension data just have three value to represent cat, dog and rabbit(one value for each class), but the high-dimension data, it have many value(like feature map) for each class, and on the high-dimension case, softmax seems have no work for this.</p>
  
  <p>so I wonder what's the different between softmax, logistic and SVM. Can anybody help? thank you!</p>