Applications of Support Vector Machines in Chemistry

By Ovidiu Ivanciuc. 2007

399 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/177d8c762e3fdbd509a4d76dc0a0cc85702f9750

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. Yizeng Liang
2. D. Abaye
3. Jonathan Romero
4. W. P. Filho
5. Qian-Nan Hu
6. Alán Aspuru-Guzik
7. S. Liu
8. Isabel Santana

In Computational Theory and Mathematics

1. Alán Aspuru-Guzik
2. K. Müller
3. Kieron Burke
4. D. Abaye
5. Jonathan Romero
6. W. P. Filho
7. Qian-Nan Hu
8. S. Liu

In Spectroscopy

1. Alán Aspuru-Guzik
2. D. Abaye
3. Jonathan Romero
4. W. P. Filho
5. Qian-Nan Hu
6. S. Liu
7. Isabel Santana
8. Dina Silva

Citations in the corpus

• Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy (2017, methodology): “SVM is a group of supervised learning methods that can be applied to classification or regression analysis, with several applications in many scientific areas (Ivanciuc, 2007).”
• Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests (2011, methodology): “Several authors propose that data mining classifiers have higher accuracy and lower error rates than the traditional classification methods [22,25,28,29].”
• Potential of quantum computing for drug discovery (2018, methodology): “Since its inception, the kernel-based SVM approach has become one of the most popular methods for building classification and regression structure-activity models [170, 171].”
• Computational Methods in Drug Discovery (2014, methodology): “explicit transformation and preprocessing using feature functions (Ivanciuc, 2007).”
• Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, methodology): “, chromatography-mass spectrometry and the neutron magnetic resonance [43].”
• Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives (2015, background): “[11] Ovidiu Ivanciuc, “Applications of support vector machines in chemistry,” in Reviews in Computational Chemistry , Vol.”
• In silico toxicity prediction by support vector machine and SMILES representation-based string kernel (2012, methodology): “The support vector machine (SVM), which was originally developed by Vapnik and co-workers, has shown promising capability for solving a number of chemical classification problems [19–21].”