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We describe and analyze a new algorithm for agnostically learning kernel-based halfspaces with respect to the \emph{zero-one} loss function.
The evaluation and estimation of the coefficients in the chebyshev series expansion of a function
D. Elliot · 1964
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R.E. Schapire · 1990
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Toward efficient agnostic learning
M. J. Kearns, R. E. Schapire, and L. M. Sellie · 1992
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For valid generalization, the size of the weights is more important than the size of the network
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Statistical Learning Theory
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Efficient learning of linear perceptrons
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Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms
O. Bousquet · 2002
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Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond
B. Schölkopf and A. J. Smola · 2002
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Reproducing Kernel Hilbert Spaces in Probability and Statistics
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Chebyshev Polynomials
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Simplified PAC-Bayesian margin bounds
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Optimal aggregation of classifiers in statistical learning
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Statistical behavior and consistency of classification methods based on convex risk minimization
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Agnostically learning halfspaces
New results for learning noisy parities and halfspaces
V. Feldman, P. Gopalan, S. Khot, and A.K. Ponnuswami · 2006
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Hardness of learning halfspaces with noise
V. Guruswami and P. Raghavendra · 2006
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Cryptographic hardness for learning intersections of halfspaces
Adam R. Klivans and Alexander A. Sherstov · 2006
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Fast rates for support vector machines using gaussian kernels
I. Steinwart and C. Scovel · 2007
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Polynomial regression under arbitrary product distributions
E. Blais, R. O’Donnell, and K Wimmer · 2008
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The tradeoffs of large scale learning
L. Bottou and O. Bousquet · 2008
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A. Kalai, A.R. Klivans, Y. Mansour, and R. Servedio · 2005
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Convexity, classification, and risk bounds
P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe · 2006
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Alternative measures of computational complexity
S. Ben-David · 2006
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On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
S.M. Kakade, K. Sridharan, and A. Tewari · 2008
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SVM optimization: Inverse dependence on training set size
S. Shalev-Shwartz and N. Srebro · 2008
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Agnostically learning halfspaces with margin errors
S. Shalev-Shwartz, O. Shamir, and K. Sridharan · 2009
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