Nonlinear Support Vector Machine Visualization for Risk Factor Analysis Using Nomograms and Localized Radial Basis Function Kernels

By Baek Hwan Cho, Hwanjo Yu, Jong Shill Lee, Y. Chee, In Young Kim, Sun I. Kim. 2008

71 citations recorded by the source corpus.

Subfields: Sociology and Political Science

How researchers see this paper

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

In Sociology and Political Science

1. Paulo Cortez
2. Giulia Vilone
3. Longo Luca
4. Mark J. Embrechts

Citations in the corpus

• Opening black box Data Mining models using Sensitivity Analysis (2011, background): “Yet, most of these graphical techniques are specific to a given learning method or DM goal (e.g. classification).”
• Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “[302, 303, 304] introduced a method for automatically generating nomograms as the graphical tool for visual explanations of the inferential mechanisms of SVM and naı̈ve bayesian-driven models.”
• Using sensitivity analysis and visualization techniques to open black box data mining models (2013, methodology): “This includes methods such as: Neural Networks (NN) (e.g., multilayer perceptrons and radial basis-functions) [18], Support Vector Machines (SVM) and other kernel-based methods [10], and ensembles, including Random Forests (RF) [2], where multiple models are combined to achieve a better predictive…”