Computer Processing of Remotely-Sensed Images: An Introduction
Environmental remote sensing is the measurement, from
a distance, of the spectral features of the Earth’s surface
and atmosphere. These measurements are normally
made by instruments carried by satellites or aircraft, and
are used to infer the nature and characteristics of the
land or sea surface, or of the atmosphere, at the...
Large-Scale Kernel Machines (Neural Information Processing series)
Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to...
This book will help you use the amazing resource that is Google Maps to your own ends. From showing maps on mobiles to creating GIS applications, this lively, recipe-packed guide is all you need.
Add to your website's functionality by utilizing Google Maps' power
Semi-Supervised Learning (Adaptive Computation and Machine Learning) In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are... Getting Up To Speed: The Future Of Supercomputing High-performance computing is important in solving complex problems in areas from climate and biology to national security. Several factors have led to the recent reexamination of the rationale for federal investment in research and development in support of high-performance computing, including continuing changes in the various component... Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The...
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