FEATURE REDUCTION METHOD FOR DECISION MACHINES

摘要

A method for feature reduction in a training set for a learning machine such as a Support Vector Machine (SVM). In one embodiment the method includes a step (35) of receiving input training data vectors xi of a training set. The input training data vectors are typically derived from a set of features in a feature space. At step (37) the input data vectors are mapped into a multi-dimensional space. At step (39) a least squares problem, derived from a formulation of the SVM, is solved to determine which features comprising the training vectors are to be deemed significant. At step (41) decision parameters and vectors of the chosen decision machine, e.g. SVM, are determined using the features determined to be significant in step (39).