Molecules are complex dynamic objects that can exist in different molecular forms
(conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known …

Pattern recognition is concerned with the development of systems that learn to solve a given
problem using a set of example instances, each represented by a number of features. These …

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 multi-instance learning, each learning object consists of many descriptive instances. In the
corresponding classification problems, each training object is labeled, but its constituent …

This book is based on my Ph. D. dissertation completed at Ghent University (Belgium) and
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …

In multi-instance learning (MIL), labels are associated with bags rather than the instances in
the bag. Most of the previous MIL methods assume that each bag has the actual label in the …

As modern complex neural networks keep breaking records and solving harder problems,
their predictions also become less and less intelligible. The current lack of interpretability …

Multiple instance learning (MIL) was a weakly supervised learning approach that sought to
assign binary class labels to collections of instances known as bags. However, due to their …

In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather
than individual feature vectors. In this paper, we address the problem of how these bags can …

Background Many computational approaches have been used for target prediction,
including machine learning, reverse docking, bioactivity spectra analysis, and chemical …