Convex optimization for the design of learning machines

This paper reviews the recent surge of interest in convex optimization in a context of pattern recognition and machine learning. The main thesis of this paper is that the design of task-specific learning machines is aided substantially by using a convex optimization solver as a back-end to implement the task, liberating the designer from the concern of designing and analyzing an ad hoc algorithm. The aim of this paper is twofold: (i) it phrases the contributions of this ESANN 2007 special session in a broader context, and (ii) it provides a road-map to published results in this context.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
• Keywords: Ad Hoc Algorithm, Convex Optimization, ESANN 2007, Neural Networks, Task-specific Learning Machines