Support vector machines for classification and regression.

Area of Science:

• Machine Learning
• Chemometrics
• Data Analysis

Background:

• Growing interest in Support Vector Machines (SVMs) over the last 15 years.
• Need for robust classification and regression techniques in scientific analysis.

Purpose of the Study:

• To describe and illustrate various Support Vector Machine (SVM) methods.
• To showcase SVM applications in diverse scientific case studies.
• To explain multiclass implementations and one-class Support Vector Domain Description (SVDD).

Main Methods:

• Illustrated SVM principles using simulated and experimental case studies.
• Demonstrated two-class classification with visualizations of learning machines, kernels, and penalty functions.
• Explored multiclass SVMs (one vs. all, one vs. one, fuzzy rules, DAG trees) and Support Vector Regression (SVR).

Main Results:

• Illustrated the impact of penalty error and radial basis function radius on SVM models.
• Showcased practical applications in mass spectrometry, near-infrared analysis, thermal analysis, and UV/Vis spectroscopy.
• Highlighted SVR's utility in multivariate calibration, especially with outliers and non-linear data.

Conclusions:

• SVMs provide versatile and effective solutions for complex classification and regression tasks.
• The study demonstrates the broad applicability of SVMs across various scientific domains.
• SVDD and SVR offer valuable extensions for specialized data analysis challenges.