A Piecewise Linear Network Classifier

— A piecewise linear network is discussed which classifies N-dimensional input vectors. The network uses a distance measure to assign incoming input vectors to an appropriate cluster. Each cluster has a linear classifier for generating class discriminants. A training algorithm is described for generating the clusters and discriminants. Theorems are given which relate the network’s performance to that of nearest neighbor and k-nearest neighbor classifiers. It is shown that the error approaches Bayes error as the number of clusters and patterns per cluster approach infinity.

• Type: Conference
• Year: 2007
• Where: IJCNN
• Authors: A. A. Abdurrab, Michael T. Manry, Jiang Li, Sanjeev S. Malalur, R. G. Gore
• Keywords: IJCNN 2007, Incoming Input Vectors, Input Vectors, N-dimensional Input Vectors