Support Vector Machines (SVM) as a Technique for Solvency Analysis

By L. Auria, R. Moro. 2008

406 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f3bbeac4b7bbe151be8c60a6abd3b78e7f9a164c

How researchers see this paper

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

In Accounting

1. Joaquim R. Martins
2. Ji-chao Li
3. Xiaosong Du

In Artificial Intelligence

1. Shanika A. Karunasekera
2. Sarah Monazam Erfani
3. Joaquim R. Martins
4. Ji-chao Li
5. Sutharshan Rajasegarar
6. C. Leckie
7. Xiaosong Du
8. Sanjay Chawla

In Management Science and Operations Research

1. Joaquim R. Martins
2. Ji-chao Li
3. Xiaosong Du

Citations in the corpus

• High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016, background): “SVMs are theoretically appealing for the following reasons [11,12]: they provide good generalisation when the parameters are appropriately configured, even if the training set has some bias; they deliver a unique solution, since the loss function is convex; and in principal they can model any traini”
• Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “As a commonly used ML approach, SVM mainly has the following advantages and disadvantages [131].”