Partial multi-label learning (PML) deals with problems where each instance is assigned with
a candidate label set, which contains multiple relevant labels and some noisy labels. Recent …

In many real world applications, the concerned objects are with multiple labels, and can be
represented as a bag of instances. Multi-instance Multi-label (MIML) learning provides a …

In image analysis, image samples are always represented by multiple view features and
associated with multiple class labels for better interpretation. However, multiple view data …

Abstract Machine learning is a field which studies how machines can alter and adapt their
behavior, improving their actions according to the information they are given. This field is …

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 …

In many real-world tasks, particularly those involving data objects with complicated
semantics such as images and texts, one object can be represented by multiple instances …

In real world applications, complex objects are usually with multiple labels, and can be
represented as multiple modal representations, eg, the complex articles contain text and …

Multi-label classification has been successfully applied to image annotation, information
retrieval, text categorization, etc. When the number of classes increases significantly, the …

Complex objects are usually with multiple labels, and can be represented by multiple modal
representations, eg, the complex articles contain text and image information as well as …

Multiview multi-instance multilabel learning (M3L) is a popular research topic during the past
few years in modeling complex real-world objects such as medical images and subtitled …