For some applications of k-nearest neighbor classiiers, the best results are obtained at a relatively large value of k. With the majority voting method, these results can be suboptimal. In this paper the performance of various weighted voting methods is tested on a number of machine learning datasets. The results show that weighted voting is often superior to majority voting, and that the linear weighting function proposed by Dudani 5] often yields slightly better results than the inverse distance function that has commonly been used in more recent work.

An empirical re-examination of weighted voting for κ-nn

A Numeral Character Recognition Using the PCA Mixture Model

Image and Video Quality Assessment Using Neural Network and SVM

Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in image classification problems has not been deeply investigated. In this study, we introduce a series of novel image classification algorithms based on CW-SSIM and use handwritten digit and face image recognition as examples for demonstration, including CW-SSIM based nearest neighbor method, CW-SSIM based k means method, CW-SSIM based support vector machine method (SVM) and CW-SSIM based SVM using affinity propagation. Among the proposed approaches, the best compromise between accuracy and complexity is obtained by the CW-SSIM support vector machine algorithm, which combines an unsupervised clustering method to divide the training images into clusters with representative images and a supervised learning method based on support vector machines to maximize the classification accuracy. Our experiments show that such a conceptually simple image classification method, which does not involve any registration, intensity normalization or sophisticated feature extraction processes, and does not rely on any modeling of the image patterns or distortion processes, achieves competitive performance with reduced computational cost.

Yang Gao

Papers

Complex-Wavelet Structural Similarity Based Image Classification