![]() ![]() Its basic idea is to transform the signal into a higher-dimensional feature space and find the optimal hyperplane for classification and regression problems. Briefly, SVM is a universal constructive learning procedure based on statistical learning theory. Support Vector Machines (SVMs) are one of the most popular ones among these techniques, initially developed for classification tasks and lately has been extended to the domain of regression. In the recent years, many different techniques have been investigated to solve the regression problems. However, there are two main difficulties facing regression problems: accuracy and computational time. In many application fields, the regression problem is widely been paid attention to, in order to predict a dependent variable (target) from a number of independent variables (observations), or to model numerical data consisting of values of variables (input) and of one or more variable (output). In summary, the most significant characteristic of this method is that it provides good performance and a comparatively simple model at extremely high-learning speed. Since computational time of this method is small, this paper presents a strategy using OP-KNN to perform Variable Selection which is tested successfully on eight real-life data sets from different application fields. Multiresponse Sparse Regression (MRSR) is used in order to rank each kth nearest neighbor and finally Leave-One-Out estimation is used to select the optimal number of neighbors and to estimate the generalization performances. It builds a one hidden-layer feedforward neural network using K-Nearest Neighbors as kernels to perform regression. ![]() This paper presents a methodology named Optimally Pruned K-Nearest Neighbors (OP-KNNs) which has the advantage of competing with state-of-the-art methods while remaining fast. ![]()
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