# Alternative to support vector machine?

I have to make comparison between 155 image feature vectors. Every feature vector has got 5 features. My image are divided in 10 classes. Unfortunately for using support vector machine i need at least 100 images for class, There is any alternative?

• For automatic border creation, you can use alternatives such as NN's or something simpler, like logistic regression. I am assuming you have about 15 images per class? Mar 2 '13 at 18:11
• Who said you must have at least 100 images per class for an SVM? It is usually good to have more data, but you can always try it with what you have and see what happens.
– Dima
Mar 4 '13 at 16:45

Why not just stick with something simpler like k-nearest neighbors or (learning) vector quantization (PDF)?

• Peter, I dont think KNNs will help here, because they help you classify data, whereas the OP is looking for ways to created the borders given the classifications already. (ie, he wants supervised learning, VS unsupervised learning). Mar 2 '13 at 19:53
• Mohammad, you may be right. I interpreted i need at least 100 images for class to mean "I need at least 100 images for classification", which is certainly not the case for KNN or VQ. It seems to me the the OP is asking for other techniques to classify data. I gave two options. Perhaps the OP can clarify?
– Peter K.
Mar 3 '13 at 0:55