Support Vector Machines (SVM)

Machine Learning has become an integral part of various applications, from image recognition to natural language processing. One popular algorithm used in the field of Machine Learning is Support Vector Machines (SVM). SVM is a supervised learning approach that can be used for both regression and classification tasks.

What is SVM?

Support Vector Machines, introduced by Vapnik et al. in 1995, is a robust and versatile algorithm that seeks to find an optimal hyperplane in a high-dimensional feature space. The objective of SVM is to classify data by finding the best possible decision boundary that separates different classes.

The key idea behind SVM is to maximize the margin between the decision boundary and the training data points. The decision boundary is a hyperplane that splits the feature space into separate regions, each corresponding to a different class. SVM selects the decision boundary with the maximum margin to achieve the best generalization on unseen data.

How does SVM work?

  1. Training Phase:

    • SVM takes a set of labeled training samples, each belonging to one of the predefined classes.
    • It maps the input samples into a high-dimensional feature space using a kernel function.
    • SVM then finds the hyperplane that maximizes the margin between the classes.
    • The samples that lie on the border of the margin are called support vectors. They play a crucial role in defining the decision boundary.
  2. Classification Phase:

    • Once the model is trained, SVM can classify new, unlabeled samples.
    • It maps the new samples into the same feature space.
    • SVM predicts the class of the new samples based on which side of the decision boundary they fall.

Advantages of SVM

  1. Effective in high-dimensional spaces: SVM performs well even when the number of features is greater than the number of samples. It is particularly useful in text classification, image classification, and bioinformatics.
  2. Robust against overfitting: SVM maximizes the margin between classes, making it less sensitive to outliers and noise in the training data.
  3. Versatile kernel functions: SVM supports various kernel functions, such as linear, polynomial, radial basis function (RBF), and sigmoid. These kernel functions enable SVM to nonlinearly map the samples into a high-dimensional space.

Disadvantages of SVM

  1. High computational complexity: Training time increases significantly with large datasets.
  2. Difficult to interpret: SVM models are not easily interpretable. Understanding the importance of different features requires additional analysis.


Support Vector Machines (SVM) is a powerful algorithm that is widely used in the field of Machine Learning. It can efficiently solve both classification and regression problems, providing accurate predictions even in high-dimensional spaces. Despite its computational complexity and interpretability challenges, SVM remains a popular choice due to its robustness and versatility.

If you are interested in learning more about SVM and its implementation in Python, consider enrolling in a 'Machine Learning using Python' course. Understanding SVM's working principles and exploring its applications will enhance your knowledge and skills in the exciting field of Machine Learning.

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