In this paper, we demonstrated how machine learning can be integrated in the oil spill monitoring field for thickness estimation. A support vector regression model was trained on reflectivities ...

This paper describes the approach to predict the layer thickness using a state-of-the-art Machine Learning regression algorithm: Support Vector Regression. The recent extension of Support Vector ...

We employed two classical machine learning algorithms, Logistic Regression (LR) and Support Vector Machine (SVM), to build predictive models. Dataset The dataset used in this project is the "Wine ...

If your database doesn’t already support internal machine learning, it’s likely that you can add that capability using MindsDB, which integrates with a half-dozen databases and five BI tools.

The diagram in Figure 2 gives you a rough idea of support vector regression for a scenario where there is just one predictor variable x. Each dot is a training data item. The red line is the linear ...

The existing manually constructed model and its modification, as well as traditional machine learning algorithms such as Artificial Neural Networks, K-nearest neighbors, Linear Regression, Support ...

Learning Vector Quantization, aka LVQ (for both classification and regression) Support Vector Machines, aka SVM (for binary classification) Random Forests, a type of “bagging” ensemble ...