Support Vector Machines (1)
The majority of learning algorithms minimize the so-called empirical risk and do not possess guaranteed generalization properties.
Support Vector Machines (SVMs) is a state-of-the-art pattern recognition technique whose foundations are stemming from statistical learning theory
SVMs can handle also the other two learning problems, i.e., regression estimation and density estimation, making them prime candidates for the abstract data processing model.