Can I read Dataset Shift in Machine Learning (Neural Information Processing series) on EtoBox?

Dataset Shift in Machine Learning (Neural Information Processing series) by Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, Neil D. Lawrence is a nonfiction available to read on EtoBox.

What is Dataset Shift in Machine Learning (Neural Information Processing series) about?

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 proble

Who reads Dataset Shift in Machine Learning (Neural Information Processing series)?

Author

Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, Neil D. Lawrence

Publisher

The MIT Press

Published

2008

Language

EN

ISBN

9780262545877

Category

nonfiction

Subjects

Science, Computer Science, Stem