Local models and Gaussian mixture models for statistical data processing
暂无分享,去创建一个
[1] S. Newcomb. A Generalized Theory of the Combination of Observations so as to Obtain the Best Result , 1886 .
[2] K. Pearson. Contributions to the Mathematical Theory of Evolution , 1894 .
[3] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[4] G. E. Peterson,et al. Control Methods Used in a Study of the Vowels , 1951 .
[5] T. W. Anderson. ASYMPTOTIC THEORY FOR PRINCIPAL COMPONENT ANALYSIS , 1963 .
[6] J. Kruskal. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .
[7] V. Hasselblad. Estimation of parameters for a mixture of normal distributions , 1966 .
[8] R. Plomp,et al. Dimensional analysis of vowel spectra , 1967 .
[9] L. Baum,et al. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .
[10] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[11] E. B. Andersen,et al. Modern factor analysis , 1961 .
[12] R. Plomp,et al. Perceptual and physical space of vowel sounds. , 1969, The Journal of the Acoustical Society of America.
[13] J. Wolfe. PATTERN CLUSTERING BY MULTIVARIATE MIXTURE ANALYSIS. , 1970, Multivariate behavioral research.
[14] L. Baum,et al. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .
[15] Keinosuke Fukunaga,et al. An Algorithm for Finding Intrinsic Dimensionality of Data , 1971, IEEE Transactions on Computers.
[16] R. Plomp,et al. Frequency analysis of Dutch vowels from 50 male speakers. , 1973, The Journal of the Acoustical Society of America.
[17] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[18] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[19] J. Gower,et al. Methods for statistical data analysis of multivariate observations , 1977, A Wiley publication in applied statistics.
[20] Allen Gersho,et al. Asymptotically optimal block quantization , 1979, IEEE Trans. Inf. Theory.
[21] Robert M. Gray,et al. An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..
[22] M. Aitkin,et al. Mixture Models, Outliers, and the EM Algorithm , 1980 .
[23] Murray Aitkin,et al. Statistical Modelling of Data on Teaching Styles , 1981 .
[24] Biing-Hwang Juang,et al. Multiple stage vector quantization for speech coding , 1982, ICASSP.
[25] Erkki Oja,et al. Subspace methods of pattern recognition , 1983 .
[26] Thomas Kailath,et al. Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..
[27] R. Cranley,et al. Multivariate Analysis—Methods and Applications , 1985 .
[28] G. McLachlan,et al. Estimation of Allocation Rates in a Cluster Analysis Context , 1985 .
[29] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[30] J. Schmee. An Introduction to Multivariate Statistical Analysis , 1986 .
[31] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[32] Farmer,et al. Predicting chaotic time series. , 1987, Physical review letters.
[33] A. Lapedes,et al. Nonlinear Signal Processing Using Neural Networks , 1987 .
[34] A. Lapedes,et al. Nonlinear signal processing using neural networks: Prediction and system modelling , 1987 .
[35] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[36] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .
[37] Terence D. Sanger,et al. An Optimality Principle for Unsupervised Learning , 1988, NIPS.
[38] Paul W. Munro,et al. Principal Components Analysis Of Images Via Back Propagation , 1988, Other Conferences.
[39] P. Foldiak,et al. Adaptive network for optimal linear feature extraction , 1989, International 1989 Joint Conference on Neural Networks.
[40] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[41] J. E. Glynn,et al. Numerical Recipes: The Art of Scientific Computing , 1989 .
[42] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.
[43] D. Broad,et al. Formant estimation by linear trans-formation of the lpc cepstrum , 1989 .
[44] Ken-ichi Funahashi,et al. On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.
[45] J. Rubner,et al. A Self-Organizing Network for Principal-Component Analysis , 1989 .
[46] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[47] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[48] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[49] Sun-Yuan Kung,et al. A neural network learning algorithm for adaptive principal component extraction (APEX) , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[50] Farrell,et al. Characterizing attractors using local intrinsic dimensions calculated by singular-value decomposition and information-theoretic criteria. , 1990, Physical review. A, Atomic, molecular, and optical physics.
[51] Garrison W. Cottrell,et al. EMPATH: Face, Emotion, and Gender Recognition Using Holons , 1990, NIPS.
[52] Todd K. Leen,et al. Hebbian feature discovery improves classifier efficiency , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[53] John E. Moody,et al. Note on Learning Rate Schedules for Stochastic Optimization , 1990, NIPS.
[54] H Hermansky,et al. Perceptual linear predictive (PLP) analysis of speech. , 1990, The Journal of the Acoustical Society of America.
[55] Terrence J. Sejnowski,et al. SEXNET: A Neural Network Identifies Sex From Human Faces , 1990, NIPS.
[56] M. Arozullah,et al. Higher order data compression with neural networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[57] James D. Keeler,et al. Predicting the Future: Advantages of Semilocal Units , 1991, Neural Computation.
[58] Guy A. Dumont,et al. Classification of acoustic emission signals via Hebbian feature extraction , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[59] Norman Yarvin,et al. Networks with Learned Unit Response Functions , 1991, NIPS.
[60] Steven J. Nowlan,et al. Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures , 1991 .
[61] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[62] David J. Marchette,et al. Adaptive mixtures: Recursive nonparametric pattern recognition , 1991, Pattern Recognit..
[63] M. Kramer. Nonlinear principal component analysis using autoassociative neural networks , 1991 .
[64] J. Freidman,et al. Multivariate adaptive regression splines , 1991 .
[65] T. Leen. Dynamics of learning in linear feature-discovery networks , 1991 .
[66] Allen Gersho,et al. Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.
[67] Garrison W. Cottrell,et al. Non-Linear Dimensionality Reduction , 1992, NIPS.
[68] John E. Moody,et al. Fast Pruning Using Principal Components , 1993, NIPS.
[69] S. Hanson,et al. Some Solutions to the Missing Feature Problem in Vision , 1993 .
[70] Zoubin Ghahramani,et al. Solving inverse problems using an EM approach to density estimation , 1993 .
[71] Stephen M. Omohundro,et al. Surface Learning with Applications to Lipreading , 1993, NIPS.
[72] Michael I. Jordan,et al. Supervised learning from incomplete data via an EM approach , 1993, NIPS.
[73] Nanda Kambhatla,et al. Fast Non-Linear Dimension Reduction , 1993, NIPS.
[74] Volker Tresp,et al. Training Neural Networks with Deficient Data , 1993, NIPS.
[75] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[76] Terrence J. Sejnowski,et al. A Mixture Model System for Medical and Machine Diagnosis , 1994, NIPS.
[77] Stephen M. Omohundro,et al. Nonlinear Image Interpolation using Manifold Learning , 1994, NIPS.
[78] Geoffrey E. Hinton,et al. Recognizing Handwritten Digits Using Mixtures of Linear Models , 1994, NIPS.
[79] Larry P. Heck,et al. Gaussian mixture model classifiers for machine monitoring , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.
[80] Michael I. Jordan,et al. Learning from Incomplete Data , 1994 .
[81] Ronald A. Cole,et al. Towards automatic collection of the US census , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.
[82] José L. Marroquín,et al. Measure fields for function approximation , 1995, IEEE Trans. Neural Networks.
[83] Juha Karhunen,et al. Generalizations of principal component analysis, optimization problems, and neural networks , 1995, Neural Networks.
[84] Volker Tresp,et al. Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging , 1995, NIPS.