Feature extraction and sufficient statistics in detection and classification
Esteban Real · 2002
The effectiveness of sufficient statistics as features in the detection/classification process is studied. The concept of a sufficient statistic is reviewed and an empirical method of developing an 'apparent' sufficient statistic from training data is offered. Examples of the performance enhancement achieved when using such statistics on real world data in both linear and neural network classifiers are given.