Cross Entropy Optimization of the Random Set Framework for Multiple Instance Learning

Abstract--Multiple instance learning (MIL) is a recently researched technique used for learning a target concept in the presence of noise. Previously, a random set framework for multiple instance learning (RSF-MIL) was proposed; however, the proposed optimization strategy did not permit the harmonious optimization of model parameters. A cross entropy, based optimization strategy is proposed. Experimental results on synthetic examples, benchmark and landmine data sets illustrate the benefits of the proposed optimization strategy.

• Type: Journal
• Year: 2010
• Where: ICPR
• Authors: Jeremy Bolton, Paul D. Gader
• Keywords: Computer Vision, Harmonious Optimization, ICPR 2010, Multiple Instance, Optimization Strategy