  The support vector machine (SVM) is an important class of learning machines
for function approach, pattern recognition, and time-serious prediction, etc.
It maps samples into the feature space by so-called support vectors of selected
samples, and then feature vectors are separated by maximum margin hyperplane.
The present paper presents the general vector machine (GVM) to replace the SVM.
The support vectors are replaced by general project vectors selected from the
usual vector space, and a Monte Carlo (MC) algorithm is developed to find the
general vectors. The general project vectors improves the feature-extraction
ability, and the MC algorithm can control the width of the separation margin of
the hyperplane. By controlling the separation margin, we show that the maximum
margin hyperplane can usually induce the overlearning, and the best learning
machine is achieved with a proper separation margin. Applications in function
approach, pattern recognition, and classification indicate that the developed
method is very successful, particularly for small-set training problems.
Additionally, our algorithm may induce some particular applications, such as
for the transductive inference.
