In recent years, the appearance of the broad learning system (BLS) is poised to
revolutionize conventional artificial intelligence methods. It represents a step toward building …

Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …

Conventionally, deep neural networks are trained offline, relying on a large dataset
prepared in advance. This paradigm is often challenged in real-world applications, eg online …

Although deep learning approaches have stood out in recent years due to their state-of-the-
art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall …

The lifelong learning paradigm in machine learning is an attractive alternative to the more
prominent isolated learning scheme not only due to its resemblance to biological learning …

Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …

Modern deep learning approaches have achieved great success in many vision applications
by training a model using all available task-specific data. However, there are two major …

Abstract Incremental learning using Deep Neural Networks (DNNs) suffers from catastrophic
forgetting. Existing methods mitigate it by either storing old image examples or only updating …

Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …

For many learning tasks where data is collected over an extended period of time, its
underlying distribution is likely to change. A typical example is information filtering, ie the …