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A unified framework for Regularization Networks and Support Vector Machines
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Learning with Kernel Machine Architectures
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167
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An equivalence between sparse approximation and Support Vector Machines
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2
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Faithful Representations and Moments of Satisfaction: Probabilistic Methods in Learning and Logic
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Regularization Theory and Neural Networks Architectures
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The mathematics of learning: Dealing with data
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A Review of Kernel Methods in Machine Learning
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A tutorial on support vector regression
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An introduction to boosting and leveraging
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4
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VC Theory of Large Margin Multi-Category Classifiers
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1
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A review of RKHS methods in machine learning
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The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
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12
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The Informational Complexity of Learning from Examples
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7
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Regression and Classification with Regularization
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6
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Large margin multi-category discriminant models and scale-sensitive Ψ-dimensions
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15
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Support vector machine soft margin classifiers: Error analysis
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68
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Everything Old Is New Again: A Fresh Look at Historical Approaches
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2
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Notes on PCA, Regularization, Sparsity and Support Vector Machines
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32
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A Sparse Representation for Function Approximation
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