… 10 cents per 1,000 annotations. In this paper we study strategies for scaling up multi-label
annotation, … the challenge of largescale multi-label annotation has not been addressed before. …

… multi-label evaluation; introducing a new evaluation measure and studying threshold calibration.
With one of the largest and most varied collections of multi-label … ciency and scalability. …

… In multi-label learning, this requirement is more evident than … a novel method for multi-label
annotation based on a … the feature representation and the annotation labels. The proposed …

… large-scale image annotation are further enhanced. … annotation approaches, wherein we
analyze their main drawbacks when scaling up to large-scale datasets and handling multi-label …

… In this paper, we have proposed a novel scalable label-locality driven image annotation
framework, which possesses four characteristics, namely, (1) the inter-label co-occurrence is …

… To address this problem, in this paper, we propose a scalable framework for annotation-…
concept annotation, that is, online multi-label active learning. This framework is scalable to both …

… To address this problem, in this paper, we propose a scalable framework for annotation-…
concept annotation, that is, online multi-label active learning. This framework is scalable to both …

… a multi-label feature selection method based on a Scalable Criterion for Large Label Set (SCLS)
aimed at identifying an effective feature subset for improving multi-label learning …

… on multi-label data streams, and uses it to study the performance of various methods. From
this study, we develop a multi-label Hoeffding tree with multi-label … realistic multi-label data …

… Further, pixel values are converted from the integer range {0, ..., 255} to the [0, 1] single
precision floating point range by dividing them by 255, to avoid possible feature scaling issues …