This book comprises chapters on key problems in machine learning and signal processing arenas.
The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal
Processing held at the Indraprastha Institute of Information Technology. Traditionally signal
processing and machine learning were considered to be separate areas of research. However in
recent times the two communities are getting closer. In a very abstract fashion signal
processing is the study of operator design. The contributions of signal processing had been to
device operators for restoration compression etc. Applied Mathematicians were more interested
in operator analysis. Nowadays signal processing research is gravitating towards operator
learning - instead of designing operators based on heuristics (for example wavelets) the trend
is to learn these operators (for example dictionary learning). And thus the gap between signal
processing and machine learning is fast converging. The 2014 Workshop on Machine Intelligence
and Signal Processing was one of the few unique events that are focused on the convergence of
the two fields. The book is comprised of chapters based on the top presentations at the
workshop. This book has three chapters on various topics of biometrics - two are on face
detection and one on iris recognition all from top researchers in their field. There are four
chapters on different biomedical signal image processing problems. Two of these are on
retinal vessel classification and extraction one on biomedical signal acquisition and the
fourth one on region detection. There are three chapters on data analysis - a topic gaining
immense popularity in industry and academia. One of these shows a novel use of compressed
sensing in missing sales data interpolation. Another chapter is on spam detection and the third
one is on simple one-shot movie rating prediction. Four other chapters cover various cutting
edge miscellaneous topics on character recognition software effort prediction speech
recognition and non-linear sparse recovery. The contents of this book will prove useful to
researchers professionals and students in the domains of machine learning and signal
processing.
