Least Squares Support Vector Machines (LS-SVM)
By maximising the width of the decision boundary then the generalisability of the model to new data is optimised. Rather than employ a non-linear separator such as a high-order polynomial, SVM techniques use a method to transform the feature space such that the classes do become linearly separable. Once a dataset has been organised into features and outcomes, a ML algorithm may be applied to it.
Similarly, a machine-learning model can distinguish an object in its view, such as a guardrail, from a line running parallel to a highway. When a machine-learning model is provided with a huge amount of… Read More View More Least Squares Support Vector Machines (LS-SVM)