Machine learning is currently a vast area of research with applications in a broad
range of fields such as computer vision, bioinformatics, information retrieval, natural
language processing, audio processing, data mining, and many others. Among
the variety of state-of-the-art machine learning approaches for such applications are
the similarity-based learning methods. Learning based on similarity refers to the
process of learning based on pairwise similarities between the training samples. The
similarity-based learning process can be both supervised and unsupervised, and the
pairwise relationship can be either a similarity, a dissimilarity, or a distance
function.
This book studies several similarity-based learning approaches, such as nearest
neighbor models, local learning, kernel methods, and clustering algorithms.
A nearest neighbor model based on a novel dissimilarity for images is presented in
this book. It is used for handwritten digit recognition and achieves impressive
results. Kernel methods are used in several tasks investigated in this book. First, a
novel kernel for visual word histograms is presented. It achieves state-of-the-art
performance for object recognition in images. Several kernels based on a pyramid
representation are presented next. They are used for facial expression recognition
from static images. The same pyramid representation is successfully used for text
categorization by topic. Moreover, an approach based on string kernels for native
language identification is also presented in this work. The approach achieves
state-of-the-art performance levels, while being language independent and theory
neutral. An interesting pattern can already be observed, namely that the machine
learning tasks approached in this book can be divided into two different areas:
computer vision and string processing.
Despite the fact that computer vision and string processing seem to be unrelated
fields of study, image analysis and string processing are in some ways similar.
As will be shown by the end of this book, the concept of treating image and text in a
similar fashion has proven to be very fertile for specific applications in computer
vision. In fact, one of the state-of-the-art methods for image categorization is
inspired by the bag of words representation,
