Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
The Computational Nature of Language Learning and Evolution
Learning is the mechanism by which language is transferred from old speakers to new.
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 ...
The Informational Complexity of Learning
Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework. The first concerns the problem of learning functional mappings using neural networks, followed by learning natural language grammars in the principles and parameters tradition of Chomsky. These two learning problems are seemingly very different. Neural networks are real-valued, infinite-dimensional, continuous mappings. On the other hand, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. Furthermore the research communities that work in the two ...
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...
Manifold Learning Theory and Applications
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread
Learning Theory
This book constitutes the refereed proceedings of the 18th Annual Conference on Learning Theory, COLT 2005, held in Bertinoro, Italy in June 2005. The 45 revised full papers together with three articles on open problems presented were carefully reviewed and selected from a total of 120 submissions. The papers are organized in topical sections on: learning to rank, boosting, unlabeled data, multiclass classification, online learning, support vector machines, kernels and embeddings, inductive inference, unsupervised learning, generalization bounds, query learning, attribute efficiency, compression schemes, economics and game theory, separation results for learning models, and survey and prospects on open problems.
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.
Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques
This volume contains the papers presented at the 11th International Wo- shop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2008) and the 12th International Workshop on Randomization and Computation (RANDOM 2008), which took place concurrently at the MIT (M- sachusetts Institute of Technology) in Boston, USA, during August 25–27, 2008. APPROX focuses on algorithmic and complexity issues surrounding the development of e?cient approximate solutions to computationally di?cult problems, and was the 11th in the series after Aalborg (1998), Berkeley (1999), Saarbru ̈cken (2000), Berkeley (2001), Rome (2002), Princeton (2003), Cambridge (2004), Berkeley (2005), Barce...
Competing Models of Linguistic Change
The articles of this volume are centered around two competing views on language change originally presented at the 2003 International Conference on Historical Linguistics in the two important plenary papers by Henning Andersen and William Croft. The latter proposes an evolutionary model of language change within a domain-neutral model of a 'generalized analysis of selection', whereas Henning Andersen takes it that cultural phenomena could not possibly be handled, i.e. observed, described, understood, in the same way as natural phenomena. These papers are models of succinct presentation of important theoretical framework. The other papers present and discuss additional models of change, e.g. ...
