Supervised and semi-supervised online boosting tree for industrial machine vision application

Authors:
Fan Wang;Chang Yuan;Xinyu Xu;Peter van Beek
Affiliations:
Stanford University;Sharp Laboratories of America, Inc.;Sharp Laboratories of America, Inc.;Sharp Laboratories of America, Inc.
Venue:
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Year:
2011

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Abstract

Machine learning techniques are being used extensively for knowledge discovery and data mining in industrial inspection applications. Traditionally, all of the training samples are required to be presented for training a classifier at a batch mode. However, in most industrial inspection applications, only a small amount of training samples are available initially and larger amount of them become sequentially available during online prediction. In addition, data properties could be changing over time. We propose supervised and semi-supervised online learning with a Boosting Tree (BT) to adapt and evolve the classifier in an online fashion and thus accommodate new information that becomes available sequentially in industrial inspection applications. The supervised online BT can efficiently expand and update existing BTs to add new knowledge without time consuming batch re-training. The semi-supervised BT utilizes co-training to improve classification accuracy by making use of the information in the unlabeled samples and thus reduce the labeling burden of a human operator. We also proposed compact knowledge representation such that data can be fit into limited memory and a fixed computational complexity can be maintained.