A central task of Bayesian machine learning is to infer the posterior distribution of hidden
random variables given observations and calculate expectations with respect to this …

We analyze the local Rademacher complexity of empirical risk minimization-based multi-
label learning algorithms, and in doing so propose a new algorithm for multi-label learning …

Joint modeling of the intensity of facial action units (AUs) from face images is challenging
due to the large number of AUs (30+) and their intensity levels (6). This is in part due to the …

In hierarchical classification, the output labels reside on a tree-or directed acyclic graph
(DAG)-structured hierarchy. On testing, the prediction paths of a given test example may be …

In hierarchical classification, the prediction paths may be required to always end at leaf
nodes. This is called mandatory leaf node prediction (MLNP) and is particularly useful when …

In this paper we present a framework to unify information theoretic feature selection criteria
for multi-label data. Our framework combines two different ideas; expressing multi-label …

Joint modeling of the intensity of multiple facial action units (AUs) from face images is
challenging due to the large number of AUs (30+) and their intensity levels (6). This is in part …

Most multi-label classification methods are evaluated on real datasets, which is a good
practice for comparing the performance among methods on the average scenario. Due to …

Manual annotation of facial action units (AUs) is highly tedious and time-consuming. Various
methods for automatic coding of AUs have been proposed, however, their performance is …

This paper presents different approaches to solve multi-label classification. In recent study
there exist many approaches to solve multi-label classification problems. Which are used in …