Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

As industrial systems become more complex and monitoring sensors for everything from
surveillance to our health become more ubiquitous, multivariate time series prediction is …

Deep learning has become increasingly popular in both academic and industrial areas in
the past years. Various domains including pattern recognition, computer vision, and natural …

In recent years, the means of disease diagnosis and treatment have been improved
remarkably, along with the continuous development of technology and science …

Vehicular ad hoc networks (VANETs) and the services they support are an essential part of
intelligent transportation. Through physical technologies, applications, protocols, and …

A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

This chapter provides a general introduction to the main subject matter of this work: multiple
instance or multi-instance learning. The two terms are used interchangeably in the literature …

Transfer learning and ensemble learning are the new trends for solving the problem that
training data and test data have different distributions. In this paper, we design an ensemble …