In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.