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Estimating the Support of a High-Dimensional Distribution
TLDR
The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm. Expand
Support Vector Method for Novelty Detection
TLDR
The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data and is regularized by controlling the length of the weight vector in an associated feature space. Expand
New Support Vector Algorithms
TLDR
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case. Expand
Online learning with kernels
TLDR
This paper considers online learning in a reproducing kernel Hilbert space, and allows the exploitation of the kernel trick in an online setting, and examines the value of large margins for classification in the online setting with a drifting target. Expand
Probabilistic arithmetic. I. Numerical methods for calculating convolutions and dependency bounds
TLDR
A new and general numerical method for calculating the appropriate convolutions of a wide range of probability distributions using lower and upper discrete approximations to the quantile function (the quasi-inverse of the distribution function) and has advantages over other methods previously proposed. Expand
Structural Risk Minimization Over Data-Dependent Hierarchies
TLDR
A result is presented that allows one to trade off errors on the training sample against improved generalization performance, and a more general result in terms of "luckiness" functions, which provides a quite general way for exploiting serendipitous simplicity in observed data to obtain better prediction accuracy from small training sets. Expand
Particle filtering algorithms for tracking an acoustic source in a reverberant environment
TLDR
A general framework for tracking an acoustic source using particle filters is formulated and four specific algorithms that fit within this framework are discussed, and results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room. Expand
Composite Binary Losses
TLDR
This work characterises when margin losses can be proper composite losses, explicitly show how to determine a symmetric loss in full from half of one of its partial losses, introduces an intrinsic parametrisation of composite binary losses and gives a complete characterisation of the relationship between proper losses and "classification calibrated" losses. Expand
Shrinking the Tube: A New Support Vector Regression Algorithm
TLDR
A new algorithm for Support Vector regression that automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction of the data points lie outside. Expand
Learning the Kernel with Hyperkernels
TLDR
The equivalent representer theorem for the choice of kernels is state and a semidefinite programming formulation of the resulting optimization problem is presented, which leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional. Expand
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