In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples and thereby increase speed a...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
Learning vector quantization (LVQ) constitutes a powerful and intuitive method for adaptive nearest prototype classification. However, original LVQ has been introduced based on he...
The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum...
We study the problem of learning large margin halfspaces in various settings using coresets to show that coresets are a widely applicable tool for large margin learning. A large m...