M. N. Murty – författare

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.

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 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.