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The concepts of centrality and diversity are highly important in
search algorithms, and play central roles in applications of
artificial intelligence (AI), machine learning (ML), social
networks, and pattern recognition. This work examines the
significance of centrality and diversity in representation,
regression, ranking, clustering, optimization, and classification.
The text is designed to be accessible to a broad readership.
Requiring only a basic background in undergraduate-level
mathematics, the work is suitable for senior undergraduate and
graduate students, as well as researchers working in machine
learning, data mining, social networks, and pattern recognition.
This work reviews the state of the art in SVM and perceptron
classifiers. A Support Vector Machine (SVM) is easily the most
popular tool for dealing with a variety of machine-learning tasks,
including classification. SVMs are associated with maximizing the
margin between two classes. The concerned optimization problem is a
convex optimization guaranteeing a globally optimal solution. The
weight vector associated with SVM is obtained by a linear
combination of some of the boundary and noisy vectors. Further,
when the data are not linearly separable, tuning the coefficient of
the regularization term becomes crucial. Even though SVMs have
popularized the kernel trick, in most of the practical applications
that are high-dimensional, linear SVMs are popularly used. The text
examines applications to social and information networks. The work
also discusses another popular linear classifier, the perceptron,
and compares its performance with that of the SVM in different
application areas.>
This book provides a concise but comprehensive guide to
representation, which forms the core of Machine Learning (ML).
State-of-the-art practical applications involve a number of
challenges for the analysis of high-dimensional data.
Unfortunately, many popular ML algorithms fail to perform, in both
theory and practice, when they are confronted with the huge size of
the underlying data. Solutions to this problem are aptly covered in
the book. In addition, the book covers a wide range of
representation techniques that are important for academics and ML
practitioners alike, such as Locality Sensitive Hashing (LSH),
Distance Metrics and Fractional Norms, Principal Components (PCs),
Random Projections and Autoencoders. Several experimental results
are provided in the book to demonstrate the discussed techniques'
effectiveness.
This book deals with network representation learning. It deals with
embedding nodes, edges, subgraphs and graphs. There is a growing
interest in understanding complex systems in different domains
including health, education, agriculture and transportation. Such
complex systems are analyzed by modeling, using networks that are
aptly called complex networks. Networks are becoming ubiquitous as
they can represent many real-world relational data, for instance,
information networks, molecular structures, telecommunication
networks and protein-protein interaction networks. Analysis of
these networks provides advantages in many fields such as
recommendation (recommending friends in a social network),
biological field (deducing connections between proteins for
treating new diseases) and community detection (grouping users of a
social network according to their interests) by leveraging the
latent information of networks. An active and important area of
current interest is to come out with algorithms that learn features
by embedding nodes or (sub)graphs into a vector space. These tasks
come under the broad umbrella of representation learning. A
representation learning model learns a mapping function that
transforms the graphs' structure information to a
low-/high-dimension vector space maintaining all the relevant
properties.
This book is about understanding the value of environmental
services in South Asia. It provides an overview of different
environmental problems in South Asia and examines how economic
valuation techniques can be used to assess these problems. It
brings together multiple case studies on valuation undertaken by
economists and environmental scientists from Bangladesh, India,
Pakistan, Nepal and Sri Lanka under the aegis of the South Asian
Network for Development and Environmental Economics (SANDEE). The
book addresses the challenges of valuing environmental changes that
are unique to developing countries. Each chapter starts with a
description of an environmental problem and the valuation strategy
used, followed by a discussion of estimation methods and results.
It is designed to serve as a reference book for students, teachers,
researchers, non-government organizations, and practitioners of
environmental valuation. Those interested in development and
environmental economics, and natural resource management policies,
will also find it useful.
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