Reducing bias in supervised learning
Nonparametric statistical supervised learning methods often suffer from bias caused by non-uniformity of the probability distribution of training samples. This problem is discussed in this paper and a new nonparametric neighborhood method for classification and estimation that significantly reduces the bias is proposed. Simulations exemplify the advantages, and theoretical results are noted.