Adaboosting neural networks: Application to on-line character recognition

Abstract

“Boosting” is a general method for improving the performance of any weak learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [4]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [3], in particular decision trees [1,2,5]. In this paper we use AdaBoost to improve the performances of a strong learning algorithm: a neural network based on-line character recognition system. In particular we will show that it can be used to learn automatically a great variety of writing styles even when the amount of training data for each style varies a lot. Our system achieves about 1.4% error on a handwritten digit data base of more than 200 writers.