Receptive Field Weighted Regression
暂无分享,去创建一个
[1] G. S. Watson,et al. Smooth regression analysis , 1964 .
[2] David G. Lowe,et al. Similarity Metric Learning for a Variable-Kernel Classifier , 1995, Neural Computation.
[3] G. Wahba,et al. A completely automatic french curve: fitting spline functions by cross validation , 1975 .
[4] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[5] D. Sparks,et al. Population coding of saccadic eye movements by neurons in the superior colliculus , 1988, Nature.
[6] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[7] Mark J. L. Orr,et al. Regularization in the Selection of Radial Basis Function Centers , 1995, Neural Computation.
[8] John Daugman,et al. Gabor wavelets for statistical pattern recognition , 1998 .
[9] Stefan Schaal,et al. Memory-based neural networks for robot learning , 1995, Neurocomputing.
[10] John C. Platt. A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.
[11] J. Hájek. A course in nonparametric statistics , 1969 .
[12] T. Hastie,et al. Local Regression: Automatic Kernel Carpentry , 1993 .
[13] J. Freidman,et al. Multivariate adaptive regression splines , 1991 .
[14] M. Kawato,et al. Internal representations of the motor apparatus: implications from generalization in visuomotor learning. , 1995, Journal of experimental psychology. Human perception and performance.
[15] David J. Field,et al. What Is the Goal of Sensory Coding? , 1994, Neural Computation.
[16] S. Schaal,et al. Robot juggling: implementation of memory-based learning , 1994, IEEE Control Systems.
[17] Terence D. Sanger,et al. A tree-structured adaptive network for function approximation in high-dimensional spaces , 1991, IEEE Trans. Neural Networks.
[18] R. J. Tibshirani,et al. Nonparametric Regression and Classification Part I—Nonparametric Regression , 1994 .
[19] L. Cooper,et al. When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .
[20] A. Georgopoulos. Higher order motor control. , 1991, Annual review of neuroscience.
[21] W. W. Muir,et al. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1980 .
[22] Ferdinando A. Mussa-Ivaldi,et al. Interference in Learning Internal Models of Inverse Dynamics in Humans , 1994, NIPS.
[23] Andrew W. Moore,et al. Fast, Robust Adaptive Control by Learning only Forward Models , 1991, NIPS.
[24] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[25] Gerald Sommer,et al. Dynamic Cell Structure Learns Perfectly Topology Preserving Map , 1995, Neural Computation.
[26] Jean-Jacques E. Slotine,et al. Space-frequency localized basis function networks for nonlinear system estimation and control , 1995, Neurocomputing.
[27] Stefan Schaal,et al. Assessing the Quality of Learned Local Models , 1993, NIPS.
[28] J. Friedman. A VARIABLE SPAN SMOOTHER , 1984 .
[29] J. Friedman,et al. Projection Pursuit Regression , 1981 .
[30] J. Simonoff. Multivariate Density Estimation , 1996 .
[31] Thomas Martinetz,et al. Topology representing networks , 1994, Neural Networks.
[32] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[33] D. Yeung,et al. Constructive feedforward neural networks for regression problems : a survey , 1995 .
[34] V. Mountcastle. Modality and topographic properties of single neurons of cat's somatic sensory cortex. , 1957, Journal of neurophysiology.
[35] W. Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .
[36] Bernd Fritzke,et al. Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.
[37] Helge Ritter,et al. Topology conserving mappings for learning motor tasks , 1987 .
[38] Jianqing Fan,et al. Variable Bandwidth and Local Linear Regression Smoothers , 1992 .
[39] C. R. Deboor,et al. A practical guide to splines , 1978 .
[40] Peter J. Millington,et al. Associative reinforcement learning for optimal control , 1991 .
[41] Richard A. Andersen,et al. A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons , 1988, Nature.
[42] T. Sejnowski,et al. Irresistible environment meets immovable neurons , 1997, Behavioral and Brain Sciences.
[43] C. Atkeson,et al. Learning arm kinematics and dynamics. , 1989, Annual review of neuroscience.
[44] E. Nadaraya. On Estimating Regression , 1964 .
[45] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[46] Joydeep Ghosh,et al. Ridge polynomial networks , 1995, IEEE Trans. Neural Networks.
[47] Cesare Furlanello,et al. Connectionist Speaker Normalization with Generalized Resource Allocating Networks , 1994, NIPS.
[48] G. Wahba. Spline models for observational data , 1990 .
[49] Stefan Schaal,et al. Local dimensionality reduction for locally weighted learning , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.
[50] Marcus Frean,et al. The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks , 1990, Neural Computation.
[51] W. Cleveland,et al. Smoothing by Local Regression: Principles and Methods , 1996 .
[52] E. Littmann. Generalization Abilities of Cascade Network Architectures , 1992 .
[53] Christopher G. Atkeson,et al. Using Local Models to Control Movement , 1989, NIPS.
[54] P. Kumar,et al. Theory and practice of recursive identification , 1985, IEEE Transactions on Automatic Control.
[55] Stephen Grossberg,et al. A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..
[56] C. Furlanello,et al. Combining local PCA and radial basis function networks for speaker normalization , 1995, Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing.
[57] Terrence J. Sejnowski,et al. The Computational Brain , 1996, Artif. Intell..
[58] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[59] Stefan Schaal,et al. From Isolation to Cooperation: An Alternative View of a System of Experts , 1995, NIPS.
[60] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[61] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[62] D. J. Felleman,et al. Topographic reorganization of somatosensory cortical areas 3b and 1 in adult monkeys following restricted deafferentation , 1983, Neuroscience.
[63] John G. Proakis,et al. Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..
[64] Jianqing Fan,et al. Data‐Driven Bandwidth Selection in Local Polynomial Fitting: Variable Bandwidth and Spatial Adaptation , 1995 .
[65] R. H. Myers. Classical and modern regression with applications , 1986 .
[66] Richard S. Sutton,et al. Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta , 1992, AAAI.
[67] Farmer,et al. Predicting chaotic time series. , 1987, Physical review letters.
[68] Patrick van der Smagt,et al. Approximation with neural networks: between local and global approximation , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.
[69] Richard S. Sutton,et al. Iterative Construction of Sparse Polynomial Approximations , 1991, NIPS.
[70] W. Cleveland,et al. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .
[71] F A Mussa-Ivaldi,et al. Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[72] W. Cleveland,et al. Regression by local fitting: Methods, properties, and computational algorithms , 1988 .
[73] J. Doyne Farmer,et al. Exploiting Chaos to Predict the Future and Reduce Noise , 1989 .
[74] Volker Tresp,et al. Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging , 1995, NIPS.
[75] T Poggio,et al. Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.