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Neural Information Processing series

An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs.

Key approaches in the rapidly developing area of sparse modeling, focusing on its application in fields including neuroscience, computational biology, and computer vision.

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.

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.

Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets.

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

The latest research in the development of technologies that will allow humans to communicate, using brain signals only, with computers, wheelchairs, prostheses, and other devices.

From Systems to Brains

Leading researchers in signal processing and neural computation present work aimed at promoting the interaction and cross-fertilization between the two fields.

Theory and Practice

Advances in computational geometry and machine learning that offer new methods for search, regression, and classification with large amounts of high-dimensional data.

Theory and Applications

A source book for state-of-the-art MDL, including an extensive tutorial and recent theoretical advances and practical applications in fields ranging from bioinformatics to psychology.

An overview of theoretical and computational approaches to neuroimaging.

Perception and Neural Function

A survey of probabilistic approaches to modeling and understanding brain function.

Theory and Practice

This book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling.

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research.