Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IJCNN.2007.4371222 | 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 |
Keywords | Field | DocType |
k nearest neighbor,nearest neighbor,piecewise linear,classification | k-nearest neighbors algorithm,Cluster (physics),Pattern recognition,Computer science,Infinity,Artificial intelligence,Classifier (linguistics),Linear classifier,Piecewise linear function,Machine learning,Bayes' theorem | Conference |
ISSN | Citations | PageRank |
2161-4393 | 2 | 0.45 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. A. Abdurrab | 1 | 2 | 0.45 |
| Michael T. Manry | 2 | 187 | 28.39 |
| Jiang Li | 3 | 4 | 1.49 |
| Sanjeev S. Malalur | 4 | 15 | 2.59 |
| R. G. Gore | 5 | 11 | 1.07 |