A nonparametric two-dimensional display for classification
Related Experiment Video
Updated: May 29, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
Published on: October 27, 2016
1Department of Electrical Engineering, Purdue University, West Lafayette, IN 47907.
Abstract:
A two-dimensional display whose coordinates are related to the distance to the kth-nearest neighbor of each class is presented. Applications of the display to minimum error, minimum cost, minimax, and Neyman-Pearson type classifier designs are given. The display is shown to present risk information in a manner that easily allows the specification of reject regions. Two methods of error estimation using the display, an error counting technique and a risk averaging method, are detailed. It is shown that the classifiers that result are generalizations of the standard k-NN majority vote classifier. As a result of the properties of the display, classifiers can be readily evaluated and modified. In addition, a condensing algorithm that preserves the nearest neighbor error count of any preclassified data set is described. The display is used to graphically illustrate the distance relationships that are central to the algorithm.
Related Concept Videos
Classification of Systems-II
Bar Graph
Two-Dimensional (2D) NMR: Overview
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse.
Classification of Systems-I
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
How Data are Classified: Numerical Data
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Graphical Representation of Inequalities
