A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Computation with infinite neural networks
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Training Invariant Support Vector Machines
Machine Learning
Permitted and forbidden sets in symmetric threshold-linear networks
Neural Computation
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Functions of a Complex Variable: Theory and Technique (Classics in Applied Mathematics)
Functions of a Complex Variable: Theory and Technique (Classics in Applied Mathematics)
A fast learning algorithm for deep belief nets
Neural Computation
An empirical evaluation of deep architectures on problems with many factors of variation
Proceedings of the 24th international conference on Machine learning
Backpropagation applied to handwritten zip code recognition
Neural Computation
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Deep learning via semi-supervised embedding
Proceedings of the 25th international conference on Machine learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
Efficient agnostic learning of neural networks with bounded fan-in
IEEE Transactions on Information Theory - Part 2
Sequential greedy approximation for certain convex optimization problems
IEEE Transactions on Information Theory
Recurrent kernel machines: Computing with infinite echo state networks
Neural Computation
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We introduce a new family of positive-definite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural networks with one layer of hidden units. We also show how to derive new kernels, by recursive composition, that may be viewed as mapping their inputs through a series of nonlinear feature spaces. These recursively derived kernels mimic the computation in deep networks with multiple hidden layers. We evaluate SVMs with these kernels on problems designed to illustrate the advantages of deep architectures. Compared to previous benchmarks, we find that on some problems, these SVMs yield state-of-the-art results, beating not only other SVMs but also deep belief nets.