Support Vector Machine
Support Vector Machine (SVM) is a powerful machine learning algorithm adopted for linear or nonlinear classification, regression, and even outlier detection tasks. SVMs can be adopted for a diversity of tasks, such as text classification, spam detection, handwriting identification, gene expression analysis, face detection, anomaly detection, etc.
Types of Support Vector Machine
- Linear SVM: To divide the data points into distinct classes, linear SVMs employ a linear decision boundary. Linear SVMs are ideal when the data can be properly divided along a linear path. This indicates that the data points may be completely divided into their corresponding classes by a single straight line in two dimensions or a hyperplane in three dimensions.
- Non-Linear SVM: In situations where a straight line cannot divide data into two groups, non-linear SVM can be used to classify the data (in the instance of 2D). Nonlinear SVMs may handle nonlinearly separable data by utilising kernel functions.
Advantages of Support Vector Machine
- The decision function can specify a variety of Kernel functions. Although default kernels are offered, users can optionally define their own custom kernels.
- In situations where there are more dimensions than samples, it is still useful.
- For the decision functions, several kernel functions can be supplied, as well as bespoke kernels.
- Various kernel functions and custom kernels can be provided for the decision functions.
Disadvantages of Support Vector Machine
- When there is more noise in the data set—that is, when target classes overlap—SVM performs poorly.
- The SVM will perform worse when there are more features per data point than there are training data samples.
- When there is more noise in the data set—that is, when target classes overlap—SVM performs poorly.
- There is no probabilistic justification for the classification because the support vector classifier operates by placing data points above and below the classifying hyperplane.
Support Vector Machine for Classification
There are two varieties of SVMs, and each has its own unique behaviour. The linear and non-linear SVMs are these two varieties.
The simplest SVM is linear, and it adheres to a straightforward principle. The linear combination of the input is always identical to the dot product when it is calculated between two characteristics of the input:
f(a, b) = a • b (vector dot product)
This rule does not apply to the non-linear SVM, which is an SVM. The non-linear SVM employs a kernel to calculate the output of the dot product between two input characteristics.
Support Vector Machines vs Neural Networks
Support Vector Machine (SVM) is a powerful machine learning algorithm adopted for linear or nonlinear classification, regression, and even outlier detection tasks and Neural networks, A machine learning (ML) model is made to simulate the structure and operations of the human brain. With a linear rise in the input size, an SVM’s number of parameters also increases linearly. Nevertheless, a NN does not. A neural network can have as many layers as desired, even though we only concentrated on single-layer networks here.