Patent ID: 6532305
Filing Date: 2003-03-11
Classification: G06K,G06N

Abstract:
A method utilizing machine-readable data storage of a set of machine-executable instructions for using a data processing system to perform a method of machine learning in which the learning is an assignment of a feature vector to a classification carried out by recursively creating and using first, a binary classifier tree having nodes, which nodes further comprise branch nodes and leaf nodes, and second, an Bayesian classifier and the method comprising the steps of:a. using a binary tree classifier to create a node, wherein the node comprises training data having a dataset size, the training data comprising multiple sets of feature vectors and corresponding classifications; b. hypothesizing the node just constructed is a leaf node; c. constructing a Bayesian leaf node classifier for the node; d. testing the hypothesis by applying the leaf node classifier against the training data and if the hypothesis is correct then the node is a leaf node, if the hypothesis fails, then the node is a branch node; e. creating, for each node determined to be a branch node, a branch node hyperplane comprising a hyperplane point and a hyperplane normal; f. splitting for each node determined to be a branch node, the training data for the branch node into two subsets, a left subset and a right subset, according to which side of the branch node hyperplane each element of training data resides; and g. passing the subsets to the next nodes recursively to create a tree.