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Support Vector Machine (SVM)

Support Vector Machine is a powerful, cutting-edge, supervised algorithm that excels on complex yet small datasets. Support Vector Machines, or Support Vector Machine for short, can be used for classification and regression issues, but they often perform best in the latter. 

They were well-known when they were developed in the 1990s, and with a little tweaking, they continue to be the preferred approach for a high-performing algorithm. In this article, you’ll explore what a support vector machine is, how it works, its advantages, disadvantages, and more. 

Introduction to Support Vector Machines (SVM)

One of the most well-liked methods for Supervised Learning used for Classification and Regression issues is the Support Vector Machine or Support Vector Machine. Nevertheless, it is widely applied for addressing machine learning classification issues. 

The Support Vector Machine algorithm aims to construct or create the optimum decision line or boundary that can divide n-dimensional space into classes to let us quickly classify fresh, updated data points in the near future. Do you know what this optimal decision boundary is called? A hyperplane! 

Support Vector Machine selects the extreme vectors and points that aid in creating the hyperplane. Support vectors are the name given to these extreme circumstances, and as a result, the technique is known as a Support Vector Machine. 

Mathematical Foundations of Support Vector Machines

Finding a hyperplane in N-dimensional space (N is the number of features) that clearly classifies the data points is the goal of the support vector machine algorithm. SVM, or Support Vector Machine, is a cutting-edge mathematical approach that leverages supervised machine learning for binary classification. 

It looks for the best hyperplane to divide two classes in a dataset. Support Vector Machine includes mathematically minimizing the norm of the weight vector while maximizing the margin between the classes, subject to the restriction that each data point be correctly categorized within a predetermined margin. 

In contrast to logistic regression, where the classifier is defined over all the points, Support Vector Machine is defined in terms of the support vectors alone; we don’t need to worry about other observations because the margin is made using the points closest to the hyperplane (support vectors). SVM thus benefits from certain organic speedups.

Learn More: What is Logistic Regression in Machine Learning

Let’s go through an example to better understand an SVM’s functioning. Consider a dataset that contains two classes (blue and green). The new data point should be categorized as either green or blue. To categorize these points, we can use a variety of decision boundaries, but how do we determine which is the best? 

Note: Since we chart the data points on a 2D graph, we call/refer to this decision boundary as a straight line. However, we only refer to this decision boundary as a ‘hyperplane’ when additional dimensions exist. 

The fundamental objective of Support Vector Machine is to find the best hyperplane, which is the plane that is farthest from both classes. This is accomplished by identifying various hyperplanes that best classify the labels, after which it selects the one that is either the furthest away from the data points or has the largest margin. 

Types of Support Vector Machine Algorithms

Support Vector Machine can be of two types:

Linear Support Vector Machines

You must have come across the term ‘linear separable data,’ right? It denotes the data that can be divided and classified into two groups leveraging only a single straight line. Linear Support Vector Machine classifies such data, and the classifier used is known as the Linear Support Vector Machine classifier.

Here is an example of a linear support vector machine:

• Let’s say we have a dataset with data from two different classes, like cats and dogs. 
• You can use a point on a 2D plane to represent every single data point. 
• You can further use this point to plot the data points for every class. 
• The SVM aims to find a straight line that can precisely divide the data points of the two classes. 
• The decision boundary is a line that runs directly through it. The Support Vector Machine will identify the decision border that optimizes the margin between the data points. 
• The margin separates each class’s closest data points and the decision border. 
• The support vectors are the data points most closely located at the decision border. The SVM will determine the decision boundary equation using the support vectors. 

Don’t know what Linear Regression is? Check out this Hero Vired article on Linear Regression in Machine Learning with Examples.

Nonlinear Support Vector Machines

Non-Linear Support Vector Machine is leveraged for non-linearly separated data, which implies a dataset is considered non-linear if it cannot be classified using a straight line. Non-Linear SVM classifiers are used for such datasets. 

Here is an example of a non-linear support vector machine:

The IRIS dataset, which contains observations of three different iris flower kinds, is a well-known dataset in machine learning. To be classified, each flower must be assigned to one of the three Iris species—Iris setosa, Iris versicolor, or Iris virginica. 

The three classes of flowers in the IRIS dataset cannot be precisely divided by a straight line since the dataset is not linearly separable. 

However, we can categorize the flowers using a non-linear SVM by projecting them into a higher dimensional space where they may be linearly separated. The RBF or Radial basis function kernel is one method of achieving this. The RBF kernel calculates and derives a similarity score between two data points. 

We may utilize the RBF kernel to map the data points in the IRIS dataset to a 100-dimensional space. We can categorize the flowers using a linear Support Vector Machine because the three classes of flowers are linearly separable in this space. 

Support Vector Machines for Classification

Support Vector Machine are leveraged in classification to locate the hyperplane with the greatest margin of separation between two or multiple classes of data points. The margin separates each class’s nearest data points and the hyperplane. 

SVMs are especially useful for classification jobs where the data cannot be separated linearly. SVMs may be employed in this situation to translate the data to a higher dimensional space where linear separability is possible. 

The stages involved in utilizing Support Vector Machine for classification are as follows: 

• Obtain a training set of data points with labels. 
• Pick a kernel operation. 
• The data is mapped to a higher dimensional space using the kernel function. 
• Put the training dataset into use to train the SVM model. 
• To categorize fresh data points, leverage the trained Support Vector Machine model. 

Support Vector Machines for Regression

Regression aims to identify a function that roughly describes the connection between a constant target variable and the input variables. A hyperplane that minimizes the mean squared error between the predicted and actual values is found in SVM regression to perform its function. 

A line or curve known as the hyperplane passes through the center of the data points. The SVM algorithm seeks to locate the hyperplane with the greatest distance from the data points. 

Advantages of Support Vector Machine

• SVMs can effectively handle small datasets because the boundary only needs to be defined by a minimal number of support vectors. 
• Using the kernel approach, which translates the input into a higher-dimensional space where the data becomes linearly separable, SVMs may simulate non-linear decision boundaries. 
• SVMs effectively classify new, untried data because of their high generalization performance. 
• Versatility: SVMs apply to a wide range of tasks, including classification and regression, bioinformatics, computer vision, and natural language processing. 

Disadvantages of Support Vector Machine

• For huge data sets, the support vector machine approach is unacceptable. 
• When the target classes overlap, and the data set has more soundness, it does not operate well. 
• The support vector machine has the possibility to perform poorly when there are more attributes for each data point than training data specimens. 

Conclusion

With the end of this Support Vector Machine guide, you now have a complete basic grasp of a support vector machine and its workings. We advise enrolling in an online certification course to further excel in concepts similar to SVM. 

For instance, Hero Vired’s Artificial Intelligence and Machine Learning course is an excellent solution to expand your knowledge of these machine learning concepts’ mathematical uses and real-world applications.