A Multiclass Classification Method Based on Output Design

Abstract

Output coding is a general framework for solving multiclass categorization problems. Some researchers have presented the notion of continuous codes and methods for designing output codes. However these methods are time-consuming and expensive. This paper describes a new framework, which we call Strong-to-Weak-to-Strong (SWS). We transform a “strong” learning algorithm to a “weak” algorithm by decreasing its iterative numbers of optimization while preserving its other characteristics like geometric properties and then make use of the kernel trick for “weak” algorithms to work in high dimensional spaces, finally improve the performances. An inspiring experimental results show that this approach is competitive with the other methods.