Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data

no code implementations • 13 Oct 2016 • Tobias Reitmaier, Adrian Calma, Bernhard Sick

An effective approach to reduce these costs is to apply any kind of active learning (AL) methods, as AL controls the training process of a classifier by specific querying individual data points (samples), which are then labeled (e. g., provided with class memberships) by a domain expert.

Active Learning General Classification

A New Vision of Collaborative Active Learning

no code implementations • 1 Apr 2015 • Adrian Calma, Tobias Reitmaier, Bernhard Sick, Paul Lukowicz, Mark Embrechts

Active learning (AL) is a learning paradigm where an active learner has to train a model (e. g., a classifier) which is in principal trained in a supervised way, but in AL it has to be done by means of a data set with initially unlabeled samples.

Active Learning

The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification

no code implementations • 13 Feb 2015 • Tobias Reitmaier, Bernhard Sick

We will see that this kernel outperforms the RBF kernel and other kernels capturing structure in data (such as the LAP kernel in Laplacian SVM) in many applications where partially labeled data are available, i. e., for semi-supervised training of SVM.

General Classification