A support vector machine (SVM) is a machine learning (ML) algorithm that Employs Supervised Learning models to solve complex classification, regression, and outlier detection problems. It performs optimal data transformations that determine the boundaries between data points based on predefined classes, labels, or outputs.

Since it requires supervised learning, teaching it to do complicated tasks needs human input. Think of it as learning with the help of a teacher instead of studying independently.

Contents

• What Does an SVM Do?
• How Does an SVM Work?
• What Are the Types of SVMs?
• What Are the Real-World Applications of SVMs?
• Key Takeaways

Read More about an SVM

SVMs, based on the short definition above, can’t learn without the aid of predefined data sets.

What Does an SVM Do?

An SVM primarily aims to identify a hyperplane that distinguishably segregates the data points of different classes.

In geometry, a hyperplane always has a subspace that is one dimension less than its ambient space. The subspace, in this case, is the space between two data points. The ambient space, meanwhile, is the entire space where multiple subspaces exist. Still confused?

Think of it this way. If the ambient space is two-dimensional, the subspace is one-dimensional. A three-dimensional ambient space, on the other hand, has a two-dimensional subspace, and so on.

How Does an SVM Work?

For this, we’ll use the most straightforward example possible.

Assume you have red and black labels and want to create a program that separates red from black data. In this case, you’ll need an SVM with a hyperplane or the decision boundary line dividing the categories.

The hyperplane is a line that widens the margin between the two closest labels (red and black). The distance of the hyperplane to the nearest label should be the largest to simplify data classification.

Take a look at the following diagram.

What Are the Types of SVMs?

SVMs come in two types—simple or linear and kernel or non-linear.

• Simple or linear SVM: A linear SVM is used to classify linearly separable data. The dataset can be segregated into categories or classes aided by a single straight line, making the data linearly distinct or separable. The classifier that divides the information is a linear SVM classifier. It is typically used to address classification and regression analysis problems. Our example above is of this type.

• Kernel or non-linear SVM: A non-linear SVM is used for non-linear data that can’t be segregated into distinct categories using a straight line. It uses a non-linear classifier. Non-linear data can be classified by adding features into higher dimensions rather than relying on two-dimensional space. The newly added features fit a hyperplane to separate classes or categories easily. Kernel SVMs are typically used to handle optimization problems with multiple variables.

What Are the Real-World Applications of SVMs?

SVMs are helpful in diverse fields.

• Addressing the geo-sounding problem: SVMs track a planet’s layered structure. The process entails solving inversion problems where the observations or results of issues are used to factor in the variables or parameters that produced them.

• Assessing seismic liquefaction potential: SVMs assess the effects of soil liquefaction when earthquakes occur on designing civil infrastructures like bridges.

• Protein remote homology detection: In computational biology, SVMs categorize proteins into structural and functional parameters depending on the sequence of amino acids when sequence identification seems complicated.

• Data classification: SVMs solve complex mathematical problems, particularly for data classification purposes where smoothing techniques reduce data outliers, making more identifiable patterns.

• Facial detection and expression classification: SVMs classify facial versus non-facial structures to create a square decision boundary around facial structures based on pixel intensity and classify resultant images.

• Surface texture classification: SVMs are used to classify surface images as smooth or gritty, for instance.

• Text categorization and handwriting recognition: SVMs are helpful in text categorization into predefined categories. They can classify news articles based on content—politics, business, or sports. They can also segregate emails to filter out spam and junk mail. In handwriting recognition, you can distinguish what an individual wrote compared to others or which was written by a human and which by a computer.

• Speech recognition: SVMs can distinguish words from speeches. They can also distinguish certain features and characteristics.

• Steganography detection: SVMs can determine if a digital image has been tampered with or contaminated, which aids in security-related matters for organizations or government agencies.

• Cancer detection: SVMs may be further enhanced to detect cancer in its early stages.

SVMs are useful in fields that include geography; seismology; computational biology; mathematics; facial, handwriting, and speech recognition; text categorization; steganography; and medicine.

Key Takeaways

• An SVM is an ML algorithm that employs supervised learning models to solve complex classification, regression, and outlier detection problems.
• SVMs come in two types—simple or linear and kernel or non-linear.
• SVMs are useful in fields that include geography; seismology; computational biology; mathematics; facial, handwriting, and speech recognition; text categorization; steganography; and medicine.

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