Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel

By B. Üstün, Willem J. Melssen, Lutgarde M. C. Buydens. 2006

324 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/d8f656e9a46493d316607997f7d25920341acf79

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. Xabier Ugarte-Pedrero
2. Igor Santos
3. Felix Brezo
4. Zerina Mašetić
5. Pablo García Bringas
6. Abdulhamit Subasi

In Artificial Intelligence

1. Zerina Mašetić
2. Xabier Ugarte-Pedrero
3. Igor Santos
4. Philip Adler
5. Felix Brezo
6. Sorelle A. Friedler
7. Casey Falk
8. Pablo García Bringas

In Control and Systems Engineering

1. Zerina Mašetić
2. Abdulhamit Subasi

Citations in the corpus

• Opcode sequences as representation of executables for data-mining-based unknown malware detection (2013, methodology): “– Support Vector Machines (SVM): We used a Sequential Minimal Optimization (SMO) algorithm [57] and performed experiments with a polynomial kernel [1], a normalised polynomial kernel [1], Pearson VII function-based universal kernel [79], and a Radial Basis Runction (RBF) based kernel [1].”
• Machine-learning-assisted materials discovery using failed experiments (2016, methodology): “Specifically, a SVM 30 model with a universal Pearson VII function-based kernel 31 was trained on 3,955 labelled reactions previously performed by the laboratory.”
• Congestive heart failure detection using random forest classifier (2016, methodology): “However, based on literature [36], we tested different values of ω and σ such as 0.”