Individuality- and Commonality-Based Multiview Multilabel Learning

Kernel-Based Learning of Hierarchical Multilabel Classification Models

We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimizati...

RIPML: A Restricted Isometry Property-Based Approach to Multilabel Learning

The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently. A recurring theme of these problems is that only a few labels are active in any given datapoint as compared to the total number of labels. However, only a small number of existing work take direct advantage of this inherent extreme sparsity in the label space. By ...

Case-Based Multilabel Ranking

We present a case-based approach to multilabel ranking, a recent extension of the well-known problem of multilabel classification. Roughly speaking, a multilabel ranking refines a multilabel classification in the sense that, while the latter only splits a predefined label set into relevant and irrelevant labels, the former furthermore puts the labels within both parts of this bipartition in a t...

Commonality-Based ABox Retrieval

Multilabel Learning for Automatic Web Services Tagging

Recently, some web services portals and search engines as Biocatalogue and Seekda!, have allowed users to manually annotate Web services using tags. User Tags provide meaningful descriptions of services and allow users to index and organize their contents. Tagging technique is widely used to annotate objects in Web 2.0 applications. In this paper we propose a novel probabilistic topic model (wh...