This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). By investigating the error distribution pattern of the training set, the VQ technique is applied to generate prototypes incrementally until the desired classification result is reached. Experimental results demonstrate the effectiveness of the method.
Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the… (More)
In solving pattern recognition problems, many classification methods, such as the nearest-neighbor (NN) rule, need to determine prototypes from a training set. To improve the performance of these classifiers in finding an efficient set of prototypes, this paper introduces a training sample sequence planning method. In particular, by estimating the relative… (More)
Controlling false acceptance errors is of critical importance in many pattern recognition applications , including signature and speaker veriÿcation problems. Toward this goal, this paper presents two post-processing methods to improve the performance of hyperspherical classiÿers in rejecting patterns from unknown classes. The ÿrst method uses a… (More)