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Mixture theory
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#	Item
351	Combining symbolic and numeric techniques for DL contents classification and analysis Jean-Charles LAMIREL, Yannick TOUSSAINT Abstract The goal of this article is to prove that the mixture of different classification and Add to Reading List Source URL: www.ercim.eu Language: English - Date: 2008-12-08 05:11:33 Abstract algebra Lattice theory Dimension reduction Ontology Self-organizing map Formal concept analysis Projection Competitive learning Lattice Mathematics Neural networks Mathematical analysis
352	Risk Bounds for Mixture Density Estimation massachusetts institute of technology — computer science and artificial intelligence laboratory Alexander Rakhlin, Dmitry Panchenko Add to Reading List Source URL: cbcl.mit.edu Language: English - Date: 2004-10-06 13:53:48 Estimation theory Statistical theory Information theory Maximum likelihood Contraction Exponentiation Logarithm Fourier transform Riemann hypothesis Mathematical analysis Mathematics Operator theory
353	Importance Sampling to Compute Likelihoods of Noise-Corrupted SpeechI R. C. van Dalen∗, M. J. F. Gales Engineering Department, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom Abstract One Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2013-03-25 11:19:17 Digital signal processing Fourier analysis Normal distribution Estimation theory Central limit theorem Maximum likelihood Dirac delta function Multivariate normal distribution Mixture model Statistics Mathematical analysis Probability and statistics
354	Department of Engineering 1 A theoretical bound for noise-robust speech recognition R. C. van Dalen Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2013-03-25 11:19:14 Statistical models Computer accessibility Speech recognition Estimation theory Additive white Gaussian noise Mixture model Maximum likelihood Statistics Probability and statistics Noise
355	Linear Gaussian Models for Speech Recognition Antti-Veikko Ilmari Rosti Wolfson College May 2004 Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2007-11-16 07:17:08 Hidden Markov model Kalman filter Markov chain Mixture model Expectation–maximization algorithm Maximum likelihood Generalized method of moments Dynamical system Matrix Statistics Markov models Estimation theory
356	A Gaussian Mixture Model Spectral Representation for Speech Recognition Matthew Nicholas Stuttle Hughes Hall Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2007-11-16 07:22:55 Abstract algebra Linear algebra Computational linguistics Estimation theory Formant Speech recognition Vector space Mixture model Generalized method of moments Algebra Mathematics Vectors
357	Infinite Support Vector Machines in Speech Recognition Jingzhou Yang, Rogier C. van Dalen and Mark Gales Department of Engineering, University of Cambridge Trumpington Street, Cambridge, CB2 1PZ, UK {jy308,rcv25,mjfg}@en Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2013-08-11 09:41:23 Stochastic processes Statistical classification Statistical theory Parametric model Mixture model Dirichlet process Hidden Markov model Support vector machine Pattern recognition Statistics Statistical models Machine learning
358	Augmented Statistical Models for Classifying Sequence Data Martin Layton Corpus Christi College University of Cambridge Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2008-01-16 04:35:17 Statistical classification Machine learning Markov models Hidden Markov model Fisher kernel Generative model Expectation–maximization algorithm Mixture model Maximum likelihood Statistics Estimation theory Statistical models
359	Model-Based Approaches to Handling Uncertainty M.J.F. Gales Abstract A powerful approach for handling uncertainty in observations is to modify the statistical model of the data to appropriately reflect this uncertainty. Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2012-02-10 06:33:37 Statistical inference Markov models Statistical models Multivariate normal distribution Speech recognition Hidden Markov model Additive white Gaussian noise Mixture model Linear regression Statistics Estimation theory Regression analysis
360	Infinite Support Vector Machines in Speech Recognition Jingzhou Yang, Rogier C. van Dalen and Mark Gales Department of Engineering, University of Cambridge Trumpington Street, Cambridge, CB2 1PZ, UK {jy308,rcv25,mjfg}@en Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2013-06-13 07:07:00 Stochastic processes Statistical classification Statistical theory Parametric model Mixture model Dirichlet process Hidden Markov model Support vector machine Pattern recognition Statistics Statistical models Machine learning

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