Visualisation and interpretation of Support Vector Regression models.

By B. Üstün, Willem J. Melssen, Lutgarde M. C. Buydens. 2007

164 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Martin Schlerf
2. Gunnar Rätsch
3. Andrew K. Skidmore
4. Christoffer Axelsson
5. W. Verhoef
6. Anas Miftah Fauzi
7. George Alan Blackburn

In Biomedical Engineering

1. Andrew K. Skidmore
2. Christoffer Axelsson
3. Martin Schlerf
4. W. Verhoef
5. Anas Miftah Fauzi

In Control and Systems Engineering

1. Alexander Zien
2. Gunnar Rätsch
3. Andrew K. Skidmore
4. Sören Sonnenburg

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “For instance, [77] presents an innovative approach to visualize trained SVM to extract the information content from the kernel matrix.”
• Multi-method ensemble selection of spectral bands related to leaf biochemistry (2015, unknown): “In SVMR, linear combinations are used a priori to calculate coefficients based on the reflectance values corresponding to the support vectors (Üstün et al., 2007).”
• POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors (2008, background): “The resulting normal Non-Commercial License (http:// creativecommons.org/licenses/by-nc/2.0/uk/) , provided the original work is properly cited.”
• Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression (2013, methodology): “This study followed an approach proposed by Üstün et al. (2007), in which the inner product of the support vector matrix and the support value vector (α) is plotted and interpreted in a similar manner to the PLSR coefficients.”