TCS-TR-A-06-15Date: Fri May 26 15:54:34 2006 Title: Learning-Related Complexity of Linear Ranking Functions Authors: Atsuyoshi Nakamura Contact:
Abstract. In this paper, we study learning-related complexity of linear ranking functions from n-dimensional Euclidean space to {1,2,...,k}. We show that their graph dimension, which is considered to indicate complexity of PAC learning of the class in the multiclass classification setting, is \Theta(n+k). This graph dimension is significantly smaller than the graph dimension \Omega(nk) of the class of {1,2,...,k}-valued decision-list functions naturally defined using k-1 linear discrimination functions. We also show a risk bound of learning linear ranking functions in the ordinal regression setting, which is easily shown by the similar technique used in the proof of an upper bound of their graph dimension. ©Copyright 2006 Authors |