Personal tools
You are here: Home

Search results

Did you not find what you were looking for? Try the Advanced Search for more precise search options.

837 items matching your criteria. RSS Feed
Probabilistic interpretation and Bayesian methods for Support Vector Machines [1%] by admin, 2007-01-31 11:08
SVMs can be interpreted as MAP-solutions to inference problems with Gaussian Process priors. I show how this helps with the intuitive interpretation of ...
Probabilistic methods for Support Vector Machines [1%] by admin, 2007-01-31 11:08
SVMs can be interpreted as MAP-solutions to inference problems with Gaussian Process priors. The evidence for certain hyperparameter values can then be ...
Sparse Kernel Feature Analysis [1%] by admin, 2007-01-31 11:08
SSVM: A Smooth Support Vector Machine for Classification [1%] by admin, 2007-01-31 11:08
Massive Support Vector Regression [1%] by admin, 2007-01-31 11:08
Improving support vector machines by modifying kernel functions [1%] by admin, 2007-01-31 11:08
We propose a method of modifying a kernel function to improve the performance of a support vector machine classifier. This is based on the structure of ...
An Introduction to Support Vector Machines [1%] by admin, 2007-01-31 11:08
Classification on Pairwise Proximity Data [1%] by admin, 2007-01-31 11:08
Estimating the Support of a High-Dimensional Distribution [1%] by admin, 2007-01-31 11:08
Sparse approximation using least squares support vector machines. [1%] by admin, 2007-01-31 11:08
Multiclass Least Squares Support Vector Machines [1%] by admin, 2007-01-31 11:08
Least squares support vector machine classifiers: a large scale algorithm. [1%] by admin, 2007-01-31 11:08
Least squares support vector machine classifiers [1%] by admin, 2007-01-31 11:08
Bayesian Transduction [1%] by admin, 2007-01-31 11:08
Transduction is an inference principle that takes a training sample and aims at estimating the values of a function at given points contained in the ...
Bayes Point Machines: Estimating the Bayes Point in Kernel Space [1%] by admin, 2007-01-31 11:08
From a Bayesian perspective Support Vector Machines choose the hypothesis corresponding to the largest possible hypersphere that can be inscribed in ...
Adaptive Margin Support Vector Machines for Classification Learning [1%] by admin, 2007-01-31 11:08
In this paper we propose a new learning algorithm for classificationlearning based on the Support Vector Machine (SVM) approach. Existing approaches ...
Learning a Preference Relation in IR [1%] by admin, 2007-01-31 11:08
In this paper we investigate the problem of learning a preference relation from a given set of ranked documents. We show that the Bayes's optimal ...
Classification on Pairwise Proximity Data [1%] by admin, 2007-01-31 11:08
We investigate the problem of learning a classification task on data represented in terms of their pairwise proximities. This representation does not ...
The Entropy Regularization Information Criterion [1%] by admin, 2007-01-31 11:08
Effective methods of capacity control via uniform convergence bounds for function expansions have been largely limited to Support Vector machines, ...
Robust Ensemble Learning [1%] by admin, 2007-01-31 11:08
AdaBoost and other ensemble methods have successfully been applied to a number of classification tasks, seemingly defying problems of overfitting. ...
SVMs for Histogram-Based Image Classification [1%] by admin, 2007-01-31 11:08
Model Selection for Support Vector Machines [1%] by admin, 2007-01-31 11:08
The Relevance Vector Machine [1%] by admin, 2007-01-31 11:08
The support vector machine (SVM) is a state-of-the-art technique for regression and classification, combining excellent generalisation properties with ...
A simple decomposition method for support vector machines [1%] by admin, 2007-01-31 11:08
The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection ...
Some Analysis on $\nu$-Support Vector Classification [1%] by admin, 2007-01-31 11:08
The $\nu$-support vector machines ($\nu$-SVM) for classification proposed by Sch\"olkopf et al. has the advantage of using a parameter $\nu$ on ...
Sparse Greedy Matrix Approximation for Machine Learning [1%] by admin, 2007-01-31 11:08
In kernel based methods such as Regularization Networks large datasets pose significant problems since the number of basis functions required for an ...
Support Vector Machines and the Bayes Rule in Classification [1%] by admin, 2007-01-31 11:08
The Bayes rule is the optimal classification rule if the underlying distribution of the data is known. In practice we do not know the underlying ...
Support Vector Machines for Classification in Nonstandard Situations [1%] by admin, 2007-01-31 11:08
The majority of classification algorithms are developed for the standard situation in which it is assumed that the examples in the training set come ...
Linear Discriminant and Support Vector Classiers [1%] by admin, 2007-01-31 11:08
At first glance, a Support Vector Classier appears to be nothing more than a generalized linear discriminant in a high dimensional transformed feature ...
Query Learning with Large Margin Classifiers [1%] by admin, 2007-01-31 11:08
The active selection of instances can significantly improve the generalisation performance of a learning machine. Large margin classifiers such as ...

Powered by Plone CMS, the Open Source Content Management System