This paper gives an attempt to explore the manifold in the label space for multi-label learning. Traditional label space is logical, where no manifold exists. In order to study the label manifold, the label space should be extended to a Euclidean space. However, the label manifold is not explicitly available from the training examples. Fortunately, according to the smoothness assumption that th...

........................................................................................................................................... 1 IP SECURITY (IPSEC).......................................................................................................................... 1 MULTI-PROTOCOL LABEL SWITCHING (MPLS) ............................................................................

A number of enhancements to the Label Distribution Protocol (LDP) have been proposed. Some have been implemented, and some are advancing toward standardization. It is likely that additional enhancements will be proposed in the future. This document defines a mechanism for advertising LDP enhancements at session initialization time, as well as a mechanism to enable and disable enhancements after...

Article history: Received 16 December 2014 Received in revised form 17 April 2015 Accepted 11 June 2015 Available online 23 June 2015

Classifier chain (CC) is a multi-label learning approach that constructs sequence of binary classifiers according to label order. Each classifier in the responsible for predicting relevance one label. When training label, proceeding labels will be taken as extended features. If features are highly correlated performance improved, otherwise, not influenced or even degraded. How discover correlat...