Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …

Having accurate, detailed, and up-to-date information about the location and behavior of
animals in the wild would improve our ability to study and conserve ecosystems. We …

Multi-label classification is an important yet challenging task in natural language processing.
It is more complex than single-label classification in that the labels tend to be correlated …

While deep convolutional neural networks (CNNs) have shown a great success in single-
label image classification, it is important to note that most real world images contain multiple …

Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

Machine learning has come of age. And just in case you might think this is a mere platitude,
let me clarify. The dream that machines would one day be able to learn is as old as …

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 …

Supervised learning-based methods for source localization, being data driven, can be
adapted to different acoustic conditions via training and have been shown to be robust to …

Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label
classification using partially observed labels per image, is becoming increasingly important …