Multi-label learning deals with problems where each example is represented by a single
instance while being associated with multiple class labels simultaneously. Binary relevance …

Learning to rank has recently emerged as an attractive technique to train deep convolutional
neural networks for various computer vision tasks. Pairwise ranking, in particular, has been …

Credal networks generalize Bayesian networks to allow for imprecision in probability values.
This paper reviews the main results on credal networks under strong independence, as …

Electrocardiogram (ECG) has been proved to be the most common and effective approach
to investigate the cardiovascular disease because that it is simple, non-invasive and low …

Multi-dimensional classification is a cutting-edge problem, in which the values of multiple
class variables have to be simultaneously assigned to a given example. It is an extension of …

Accurate class and attribute recognition is the critical technique to convert the unstructured
product image data into structured knowledge base, which provides strong support for …

Automatic recognition of human activities from time-series sensor data (referred to as HAR)
is a growing area of research in ubiquitous computing. Most recent research in the field …

Multi-label learning is the problem where each instance is associated with multiple labels
simultaneously. Binary Relevance (BR) is a representative algorithm for multi-label learning …

Color fundus photographs enable the observation of numerous critical biomarkers and early-
onset lesions associated with illnesses. Due to its non-invasive and cost-effective nature …

We present a unified framework called deep dependency networks (DDNs) that combines
dependency networks and deep learning architectures for multi-label classification, with a …