This book provides a general overview of multiple instance learning
(MIL), defining the framework and covering the central paradigms. The
authors discuss the most important algorithms for MIL such as
classification, regression and clustering. With a focus on
classification, a taxonomy is set and the most relevant proposals are
specified. Efficient algorithms are developed to discover relevant
information when working with uncertainty. Key representative
applications are included.
This book carries out a study of the key related fields of distance
metrics and alternative hypothesis. Chapters examine new and developing
aspects of MIL such as data reduction for multi-instance problems and
imbalanced MIL data. Class imbalance for multi-instance problems is
defined at the bag level, a type of representation that utilizes
ambiguity due to the fact that bag labels are available, but the labels
of the individual instances are not defined.
Additionally, multiple instance multiple label learning is explored.
This learning framework introduces flexibility and ambiguity in the
object representation providing a natural formulation for representing
complicated objects. Thus, an object is represented by a bag of
instances and is allowed to have associated multiple class labels
simultaneously.
This book is suitable for developers and engineers working to apply MIL
techniques to solve a variety of real-world problems. It is also useful
for researchers or students seeking a thorough overview of MIL
literature, methods, and tools.
