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An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift.ContributorsShai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the toolz library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

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[ No Hassle 30 Day Returns ][ Ships Daily ] [ Underlining/Highlighting: NONE ] [ Writing: NONE ] [ Edition: 3rd ] Publisher: FlatWorld Pub Date: 8/1/2020 Binding: Paperback Pages: 410 3rd edition.

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Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.69

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GIS for Surface Water: Using the National Hydrography Dataset enables scientists, managers, and students to understand the complexities of the vital surface waters of the United States. It triggers exploration and analysis by combining the ready-to-use powers of a comprehensive database of the nation’s waters and the ArcGIS® platform for geographic data analysis and mapping. This book explains how the water that exists on the landscape has been translated into a computer database, and how data about the water, such as chemistry and fish habitat, is linked to this database. Elevation data is also incorporated to produce watersheds at the micro and macro levels. This information, along with climatic data, leads to the creation of water flow and velocity estimates for every segment in the 7.5-million-mile drainage network in the United States. With the development of this complete water information system, the reader is poised to generate new knowledge of the behavior of surface water systems.  Application of the ArcGIS suite of geographic and network analysis tools makes the science of surface water come to life. GIS for Surface Water vividly demonstrates how the vast capabilities of ArcGIS can be easily applied to the National Hydrography Dataset and companion Watershed Boundary Dataset to reveal the intricate properties of complex water networks. A variety of GIS operations are made simpler through the use of ArcGIS. You will be able to input the National Hydrography Dataset in your GIS and trace the path of water within minutes. With just a little more effort, you will be able to create enlightening flow-volume maps showing how much water flows through any given river system. By following basic principles outlined in the book, you will discover that these methods also apply to the science of surface-water systems for any nation. User stories by experienced water analysts arte also included to help buoy your knowledge of GIS for surface water.

GIS for Surface Water: Using the National Hydrography Dataset enables scientists, managers, and students to understand the complexities of the vital surface waters of the United States. It triggers exploration and analysis by combining the ready-to-use powers of a comprehensive database of the nation’s waters and the ArcGIS® platform for geographic data analysis and mapping. This book explains how the water that exists on the landscape has been translated into a computer database, and how data about the water, such as chemistry and fish habitat, is linked to this database. Elevation data is also incorporated to produce watersheds at the micro and macro levels. This information, along with climatic data, leads to the creation of water flow and velocity estimates for every segment in the 7.5-million-mile drainage network in the United States. With the development of this complete water information system, the reader is poised to generate new knowledge of the behavior of surface water systems.  Application of the ArcGIS suite of geographic and network analysis tools makes the science of surface water come to life. GIS for Surface Water vividly demonstrates how the vast capabilities of ArcGIS can be easily applied to the National Hydrography Dataset and companion Watershed Boundary Dataset to reveal the intricate properties of complex water networks. A variety of GIS operations are made simpler through the use of ArcGIS. You will be able to input the National Hydrography Dataset in your GIS and trace the path of water within minutes. With just a little more effort, you will be able to create enlightening flow-volume maps showing how much water flows through any given river system. By following basic principles outlined in the book, you will discover that these methods also apply to the science of surface-water systems for any nation. User stories by experienced water analysts arte also included to help buoy your knowledge of GIS for surface water.

Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 2.78