Step Size Adaptation in Reproducing Kernel Hilbert Space
暂无分享,去创建一个
Alexander J. Smola | S. V. N. Vishwanathan | Nicol N. Schraudolph | Alex Smola | N. Schraudolph | S. Vishwanathan
[1] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[2] H. D. Block. The perceptron: a model for brain functioning. I , 1962 .
[3] Albert B Novikoff,et al. ON CONVERGENCE PROOFS FOR PERCEPTRONS , 1963 .
[4] Marvin Minsky,et al. Perceptrons: An Introduction to Computational Geometry , 1969 .
[5] Richard S. Sutton,et al. Goal Seeking Components for Adaptive Intelligence: An Initial Assessment. , 1981 .
[6] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[7] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[8] Luís B. Almeida,et al. Acceleration Techniques for the Backpropagation Algorithm , 1990, EURASIP Workshop.
[9] Tom Tollenaere,et al. SuperSAB: Fast adaptive back propagation with good scaling properties , 1990, Neural Networks.
[10] O. Mangasarian,et al. Robust linear programming discrimination of two linearly inseparable sets , 1992 .
[11] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.
[12] Barak A. Pearlmutter. Fast Exact Multiplication by the Hessian , 1994, Neural Computation.
[13] Manfred K. Warmuth,et al. On Weak Learning , 1995, J. Comput. Syst. Sci..
[14] Mance E. Harmon,et al. Multi-Agent Residual Advantage Learning with General Function Approximation. , 1996 .
[15] Mark Harmon. Multi-player residual advantage learning with general function , 1996 .
[16] Bernhard Schölkopf,et al. Improving the accuracy and speed of support vector learning machines , 1997, NIPS 1997.
[17] Manfred K. Warmuth,et al. Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..
[18] Yoav Freund,et al. Large Margin Classification Using the Perceptron Algorithm , 1998, COLT' 98.
[19] Nello Cristianini,et al. The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines , 1998, ICML.
[20] Yoav Freund,et al. Large Margin Classification Using the Perceptron Algorithm , 1998, COLT.
[21] Alexander J. Smola,et al. Learning with kernels , 1998 .
[22] Thibault Langlois,et al. Parameter adaptation in stochastic optimization , 1999 .
[23] Claudio Gentile,et al. The Robustness of the p-Norm Algorithms , 1999, COLT '99.
[24] Nicol N. Schraudolph,et al. Local Gain Adaptation in Stochastic Gradient Descent , 1999 .
[25] Nicol N. Schraudolph,et al. Online Independent Component Analysis with Local Learning Rate Adaptation , 1999, NIPS.
[26] Andreas Griewank,et al. Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.
[27] Claudio Gentile,et al. A New Approximate Maximal Margin Classification Algorithm , 2002, J. Mach. Learn. Res..
[28] Mark Herbster,et al. Learning Additive Models Online with Fast Evaluating Kernels , 2001, COLT/EuroCOLT.
[29] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[30] Nicol N. Schraudolph,et al. Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent , 2002, Neural Computation.
[31] Koby Crammer,et al. Ultraconservative Online Algorithms for Multiclass Problems , 2001, J. Mach. Learn. Res..
[32] Koby Crammer,et al. Online Classification on a Budget , 2003, NIPS.
[33] Alexander J. Smola,et al. Online learning with kernels , 2001, IEEE Transactions on Signal Processing.
[34] Yi Li,et al. The Relaxed Online Maximum Margin Algorithm , 1999, Machine Learning.
[35] Koby Crammer,et al. On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.
[36] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[37] Manfred K. Warmuth,et al. Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions , 1999, Machine Learning.
[38] B. Yegnanarayana,et al. Artificial Neural Networks , 2004 .
[39] Thomas Hofmann,et al. Support vector machine learning for interdependent and structured output spaces , 2004, ICML.
[40] Thomas Hofmann,et al. Hierarchical document categorization with support vector machines , 2004, CIKM '04.
[41] Thomas Hofmann,et al. Exponential Families for Conditional Random Fields , 2004, UAI.
[42] Jason Weston,et al. Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..
[43] Nicol N. Schraudolph,et al. Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation , 2005, NIPS.
[44] Bernhard Schölkopf,et al. Iterative kernel principal component analysis for image modeling , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[45] Nicol N. Schraudolph,et al. Stochastic optimisation for high-dimensional tracking in dense range maps , 2005 .
[46] Jason Weston,et al. Online (and Offline) on an Even Tighter Budget , 2005, AISTATS.
[47] Yoram Singer,et al. A New Perspective on an Old Perceptron Algorithm , 2005, COLT.
[48] Yoram Singer,et al. The Forgetron: A Kernel-Based Perceptron on a Fixed Budget , 2005, NIPS.
[49] Mark W. Schmidt,et al. Accelerated training of conditional random fields with stochastic gradient methods , 2006, ICML.
[50] R. Sutton. Gain Adaptation Beats Least Squares , 2006 .
[51] Luc Van Gool,et al. Fast stochastic optimization for articulated structure tracking , 2007, Image Vis. Comput..
[52] Iain Murray,et al. Introduction To Gaussian Processes , 2008 .