INFORMATION THEORETIC LEARNING: RENYI'S ENTROPY AND ITS APPLICATIONS TO ADAPTIVE SYSTEM TRAINING
暂无分享,去创建一个
[1] Deniz Erdogmus,et al. Lower and Upper Bounds for Misclassification Probability Based on Renyi's Information , 2004, J. VLSI Signal Process..
[2] E. Oja,et al. Independent Component Analysis , 2001 .
[3] A. K. Rigler,et al. Accelerating the convergence of the back-propagation method , 1988, Biological Cybernetics.
[4] Deniz Erdoğmuş,et al. COMPARISON OF ENTROPY AND MEAN SQUARE ERROR CRITERIA IN ADAPTIVE SYSTEM TRAINING USING HIGHER ORDER STATISTICS , 2004 .
[5] Deniz Erdogmus,et al. Generalized information potential criterion for adaptive system training , 2002, IEEE Trans. Neural Networks.
[6] Deniz Erdogmus,et al. An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems , 2002, IEEE Trans. Signal Process..
[7] Amparo Alonso-Betanzos,et al. A Global Optimum Approach for One-Layer Neural Networks , 2002, Neural Computation.
[8] José Carlos Príncipe,et al. Fast algorithm for adaptive blind equalization using order-α Renyi's entropy , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[9] Jing Zhao,et al. Simultaneous extraction of Principal Components using givens rotations and output variances , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[10] Deniz Erdogmus,et al. Blind source separation of time-varying instantaneous mixtures using an on-line algorithm , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[11] Deniz Erdogmus,et al. Entropy minimization for supervised digital communications channel equalization , 2002, IEEE Trans. Signal Process..
[12] Deniz Erdogmus,et al. A Neural Network Perspective to Extended Luenberger Observers , 2002 .
[13] Santamaria,et al. A fast algorithm for adaptive blind equalization using order-/spl alpha/ Renyi's entropy , 2002 .
[14] Deniz Erdogmus,et al. Insights on the relationship between probability of misclassification and information transfer through classifiers , 2002, Int. J. Comput. Syst. Signals.
[15] D. Erdogmus,et al. Convergence analysis of the information potential criterion in Adaline training , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).
[16] Deniz Erdoğmuş,et al. Information transfer through classifiers and its relation to probability of error , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[17] Chong-Yung Chi,et al. Cumulant-based inverse filter criteria for MIMO blind deconvolution: properties, algorithms, and application to DS/CDMA systems in multipath , 2001, IEEE Trans. Signal Process..
[18] Deniz Erdoğmuş,et al. Blind source separation using Renyi's mutual information , 2001, IEEE Signal Processing Letters.
[19] José Carlos Príncipe,et al. Optimization in companion search spaces: the case of cross-entropy and the Levenberg-Marquardt algorithm , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[20] C. E. SHANNON,et al. A mathematical theory of communication , 1948, MOCO.
[21] Deniz Erdogmus,et al. AN ON-LINE ADAPTATION ALGORITHM FOR ADAPTIVE SYSTEM TRAINING WITH MINIMUM ERROR ENTROPY: STOCHASTIC INFORMATION GRADIENT , 2001 .
[22] K. Torkkola. Visualizing class structure in data using mutual information , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).
[23] John W. Fisher,et al. Learning from Examples with Information Theoretic Criteria , 2000, J. VLSI Signal Process..
[24] Mark R. Titchener. A measure of information , 2000, Proceedings DCC 2000. Data Compression Conference.
[25] Jose C. Principe,et al. Neural and adaptive systems : fundamentals through simulations , 2000 .
[26] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[27] Christian Jutten,et al. Source separation techniques applied to linear prediction , 2000 .
[28] Richard J. Duro,et al. Discrete-time backpropagation for training synaptic delay-based artificial neural networks , 1999, IEEE Trans. Neural Networks.
[29] Chun Chen,et al. A new hybrid recurrent neural network , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).
[30] Aapo Hyvärinen,et al. Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.
[31] Jean-François Bercher,et al. Estimating the entropy of a signal with applications , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[32] B. Farhang-Boroujeny,et al. Adaptive Filters: Theory and Applications , 1999 .
[33] J. Príncipe,et al. Blind source separation using information measures in the time and frequency domains , 1999 .
[34] Aapo Hyvärinen,et al. Survey on Independent Component Analysis , 1999 .
[35] Chen-Fang Chang,et al. Observer-based air fuel ratio control , 1998 .
[36] Jean-Francois Cardoso,et al. Blind signal separation: statistical principles , 1998, Proc. IEEE.
[37] S. Amari,et al. Flexible Independent Component Analysis , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).
[38] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[39] John W. Fisher,et al. A novel measure for independent component analysis (ICA) , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[40] J. Príncipe,et al. Training neural networks with additive noise in the desired signal , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[41] S. Haykin,et al. Making sense of a complex world [chaotic events modeling] , 1998, IEEE Signal Process. Mag..
[42] W. Edmonson,et al. A global least mean square algorithm for adaptive IIR filtering , 1998 .
[43] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[44] J. Príncipe,et al. Energy, entropy and information potential for neural computation , 1998 .
[45] Durbin,et al. Biological Sequence Analysis , 1998 .
[46] William R. Saunders,et al. Adaptive Structures: Dynamics and Control , 1998 .
[47] Shun-ichi Amari,et al. Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information , 1997, Neural Computation.
[48] K. Loparo,et al. Optimal state estimation for stochastic systems: an information theoretic approach , 1997, IEEE Trans. Autom. Control..
[49] Christian Cachin,et al. Smooth Entropy and Rényi Entropy , 1997, EUROCRYPT.
[50] Prasanna K. Sahoo,et al. Threshold selection using Renyi's entropy , 1997, Pattern Recognit..
[51] J. Príncipe,et al. Nonlinear extensions to the minimum average correlation energy filter , 1997 .
[52] L. Györfi,et al. Nonparametric entropy estimation. An overview , 1997 .
[53] G. Deco,et al. An Information-Theoretic Approach to Neural Computing , 1997, Perspectives in Neural Computing.
[54] Shun-ichi Amari,et al. Neural Learning in Structured Parameter Spaces - Natural Riemannian Gradient , 1996, NIPS.
[55] B. Ripley. Pattern Recognition and Neural Networks , 1996 .
[56] Paul A. Viola,et al. Empirical Entropy Manipulation for Real-World Problems , 1995, NIPS.
[57] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[58] H. Vincent Poor,et al. A lower bound on the probability of error in multihypothesis testing , 1995, IEEE Trans. Inf. Theory.
[59] David B. Fogel,et al. Alternative Neural Network Training Methods , 1995, IEEE Expert.
[60] D. T. Kaplan,et al. Understanding Nonlinear Dynamics , 1995 .
[61] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[62] P. Gács. The Boltzmann entropy and randomness tests , 1994, Proceedings Workshop on Physics and Computation. PhysComp '94.
[63] Mohammad Bagher Menhaj,et al. Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.
[64] A. Tsybakov,et al. Root-N consistent estimators of entropy for densities with unbounded support , 1994, Proceedings of 1994 Workshop on Information Theory and Statistics.
[65] Sergio Verdú,et al. Generalizing the Fano inequality , 1994, IEEE Trans. Inf. Theory.
[66] Samy Bengio,et al. Use of genetic programming for the search of a new learning rule for neural networks , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.
[67] Andreas S. Weigend,et al. Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .
[68] Wray L. Buntine,et al. Computing second derivatives in feed-forward networks: a review , 1994, IEEE Trans. Neural Networks.
[69] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[70] C. Beck,et al. Thermodynamics of chaotic systems : an introduction , 1993 .
[71] N. Merhav,et al. Relations Between Entropy and Error Probability , 1993, Proceedings. IEEE International Symposium on Information Theory.
[72] C. L. Nikias,et al. Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .
[73] Frank Bärmann,et al. A learning algorithm for multilayered neural networks based on linear least squares problems , 1993, Neural Networks.
[74] Sandro Ridella,et al. Statistically controlled activation weight initialization (SCAWI) , 1992, IEEE Trans. Neural Networks.
[75] Chris Bishop,et al. Exact Calculation of the Hessian Matrix for the Multilayer Perceptron , 1992, Neural Computation.
[76] Etienne Barnard,et al. Optimization for training neural nets , 1992, IEEE Trans. Neural Networks.
[77] Roberto Battiti,et al. First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.
[78] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..
[79] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[80] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[81] M. A. Styblinski,et al. Experiments in nonconvex optimization: Stochastic approximation with function smoothing and simulated annealing , 1990, Neural Networks.
[82] Bernard Widrow,et al. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[83] P. Jones,et al. A Diary on Information Theory , 1989 .
[84] D. V. Gokhale,et al. Entropy expressions and their estimators for multivariate distributions , 1989, IEEE Trans. Inf. Theory.
[85] Anuradha M. Annaswamy,et al. Stable Adaptive Systems , 1989 .
[86] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[87] Raymond L. Watrous. Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization , 1988 .
[88] L. Györfi,et al. Density-free convergence properties of various estimators of entropy , 1987 .
[89] Ralph Linsker,et al. Towards an Organizing Principle for a Layered Perceptual Network , 1987, NIPS.
[90] Geoffrey E. Hinton,et al. Learning representations by back-propagation errors, nature , 1986 .
[91] S. Haykin,et al. Adaptive Filter Theory , 1986 .
[92] Jan Beirlant,et al. The empirical distribution function and strong laws for functions of order statistics of uniform spacings , 1985 .
[93] S. Qureshi,et al. Adaptive equalization , 1982, Proceedings of the IEEE.
[94] Shun-ichi Amari,et al. Differential-geometrical methods in statistics , 1985 .
[95] Bernard Widrow,et al. Adaptive Signal Processing , 1985 .
[96] P. Hall. Limit theorems for sums of general functions of m-spacings , 1984, Mathematical Proceedings of the Cambridge Philosophical Society.
[97] Simon Haykin,et al. Introduction to Adaptive Filters , 1984 .
[98] Graham C. Goodwin,et al. Adaptive filtering prediction and control , 1984 .
[99] J. Treichler,et al. A new approach to multipath correction of constant modulus signals , 1983 .
[100] P. Bickel,et al. Sums of Functions of Nearest Neighbor Distances, Moment Bounds, Limit Theorems and a Goodness of Fit Test , 1983 .
[101] Erkki Oja,et al. Subspace methods of pattern recognition , 1983 .
[102] Reuven Y. Rubinstein,et al. Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.
[103] D. Donoho. ON MINIMUM ENTROPY DECONVOLUTION , 1981 .
[104] D. Godard,et al. Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems , 1980, IEEE Trans. Commun..
[105] A. Benveniste,et al. Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communications , 1980 .
[106] M. Ben-Bassat,et al. Renyi's entropy and the probability of error , 1978, IEEE Trans. Inf. Theory.
[107] Pushpa N. Rathie,et al. On the entropy of continuous probability distributions (Corresp.) , 1978, IEEE Trans. Inf. Theory.
[108] Ibrahim A. Ahmad,et al. A nonparametric estimation of the entropy for absolutely continuous distributions (Corresp.) , 1976, IEEE Trans. Inf. Theory.
[109] D. P. Mittal. On additive and non-additive entropies , 1975, Kybernetika.
[110] Yuriy G. Dmitriev,et al. On the Estimation of Functionals of the Probability Density and Its Derivatives , 1974 .
[111] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .
[112] Alfréd Rényi,et al. Probability Theory , 1970 .
[113] F. P. Tarasenko. On the evaluation of an unknown probability density function, the direct estimation of the entropy from independent observations of a continuous random variable, and the distribution-free entropy test of goodness-of-fit , 1968 .
[114] R. Gallager. Information Theory and Reliable Communication , 1968 .
[115] L. L. Campbell,et al. A Coding Theorem and Rényi's Entropy , 1965, Inf. Control..
[116] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[117] G. A. Barnard,et al. Transmission of Information: A Statistical Theory of Communications. , 1961 .
[118] S. Kullback. Information Theory and Statistics , 1959 .
[119] Norbert Wiener,et al. Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications , 1949 .
[120] F. Attneave,et al. The Organization of Behavior: A Neuropsychological Theory , 1949 .
[121] Claude E. Shannon,et al. A Mathematical Theory of Communications , 1948 .