# RTG: Classification Methods


• 1: Support Vector Machine (SVM)
Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
• 2: Kernel Density Estimation (KDE)
Kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample.
• 3: K-Nearest Neighbors (KNN)
The k-nearest neighbors algorithm (KNN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space
• 4: Numerical Experiments and Real Data Analysis

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