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Learning features and segments from waveforms : a statistical model of early phonological acquisition
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Of the Dissertation Learning Features and Segments from Waveforms: A Statistical Model of Early Phonological Acquisition and its Applications.Abstract:
of the Dissertation Learning Features and Segments from Waveforms: A Statistical Model of Early Phonological Acquisitionread more
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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A Maximum Entropy Model of Phonotactics and Phonotactic Learning
Bruce Hayes,Colin Wilson +1 more
TL;DR: This work proposes a theory of phonotactic grammars and a learning algorithm that constructs such Grammars from positive evidence, and applies the model in a variety of learning simulations, showing that the learnedgrammars capture the distributional generalizations of these languages and accurately predict the findings of a phonotactics experiment.
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The Discovery of Spoken Language, Peter W. Jusczyk (Ed.). MIT Press (1997), ISBN 0 262 10058 4
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Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner.
TL;DR: It is argued that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible.
Inductive learning of phonotactic patterns
TL;DR: Of the Dissertation Inductive Learning of Phonotactic Patterns and its Applications to Teaching and Research: Foundations of a Response to the Response to Tocqueville's inequality.
References
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Maximum likelihood from incomplete data via the EM algorithm
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Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.