Abstract:
Multi instance multi label learning is a framework in which objects are represented as bags of instances and labels are provided at the bag level. Instance annotation is ...Show MoreMetadata
Abstract:
Multi instance multi label learning is a framework in which objects are represented as bags of instances and labels are provided at the bag level. Instance annotation is the problem of assigning labels to the instances in a bag given only the bag label. Recently, OR-ed logistic regression (OR-LR) model and an EM based inference method have been proposed for instance annotation. Due to the linear nature of the logistic regression function, OR-LR performance on linearly inseparable data is limited. This paper addresses this problem by proposing a regularized kernel-based extension to the OR-LR framework. Experiments show that the kernel-based OR-LR algorithm achieves a significant improvement in classification accuracy over the linear OR-LR from 3% to 9% on audio bird song and image annotation datasets and two synthetic datasets.
Date of Conference: 21-24 September 2014
Date Added to IEEE Xplore: 20 November 2014
Electronic ISBN:978-1-4799-3694-6