Work

Active learning with SVM for land cover classification - what can go wrong?

Year: 2022

Type: conference-paper

Abstract: Training machine learning algorithms for land cover classification is labour intensive. Applying active learning strategies tries to alleviate this, but can lead to unexpected results. We demonstrate what can go wrong when uncertainty sampling with an SVM is applied to real world remote sensing data. Possible causes and solutions are suggested.

Source: Fraunhofer-Publica (Fraunhofer-Gesellschaft)

DOI: 10.24406/publica-fhg-393458

Authors: Wuttke, Sebastian, Middelmann, Wolfgang, Stilla, Uwe

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2571925331

FWCI:

Cites: 6

Cited by:

Related to: 10