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Introduction to Semi-supervised Learning
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mi...
Elements of Dimensionality Reduction and Manifold Learning
Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, an...
The Cambridge Companion to Chomsky
This second edition discusses advances in Chomsky's science of language, his view of the human mind and its study, and his socioeconomic-political contributions.
Speech Dynamics
The relationship between diachronic change and synchronic variation at the articulatory, auditory, acoustic and social level is one of the greatest puzzles in the study of language. Even though plentiful examples exist to suggest that dynamics of synchronic variation and diachronic change are tightly interconnected, a unified theory to account for language change in its relationship to all layers of synchronic variation remains a desideratum. This volume compiles new evidence from articulatory, acoustic, auditory, sociolinguistic, and phonological analyses of segmental and prosodic data and computational modelling, and offers a refreshing theoretical angle on the ongoing debates in language ...
Stephen Smale - Reaching Higher Dimensions
In 2000, the American Mathematical Society published a biography of Professor Stephen Smale, who had recently retired from a prestigious career at the University of California, Berkeley. But in retirement, Professor Smale has continued his academic pursuits through the present day, resulting in numerous additional publications and honors in the past 20+ years. As part of the CityU Legacy Series, this book documents Professor Smale's time at City University of Hong Kong, during his first appointment as a Distinguished University Professor in the Department of Mathematics from 1995-2001 as well as when he returned from 2009-2016. It also covers colorful and adventurous aspects of his life, inc...
Advances in Neural Information Processing Systems 16
Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.
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Intelligent Signal Processing
"IEEE Press is proud to present the first selected reprint volume devoted to the new field of intelligent signal processing (ISP). ISP differs fundamentally from the classical approach to statistical signal processing in that the input-output behavior of a complex system is modeled by using "intelligent" or "model-free" techniques, rather than relying on the shortcomings of a mathematical model. Information is extracted from incoming signal and noise data, making few assumptions about the statistical structure of signals and their environment. Intelligent Signal Processing explores how ISP tools address the problems of practical neural systems, new signal data, and blind fuzzy approximators....
Journal of Machine Learning Research
An international forum covering all areas of machine learning.
