Applications of Support Vector Machines (SVM)


Support vector machines are one of the finest and most efficient Machine Learning classification algorithms out there. However, support vector machines are more popular when the dataset to work with is smaller in size. This is understandable as we know that when the size will increase the SVM will take longer to train.

One of the things about support vector machines is that they are more flexible for new data. This makes them easier to use in the applications where we need more flexibility in the training and testing data. Due to the large margin that it likes to generate, we can fit in more data and classify it perfectly.

Support vector machines are mainly supervised learning algorithms. And they are the finest algorithms for classifying unseen data. Hence they can be used in a wide variety of applications.

We will look at the applications based on the fields it impacts. Here are the ones where SVMs are used the most:

  • Image-based analysis and classification tasks
  • Geo-spatial data-based applications
  • Text-based applications
  • Computational biology
  • Security-based applications
  • Chaotic systems control

Note: The applications of the support vector machines are not limited to these categories. It is a classification as well as a regression algorithm and the uses are endless.

Image-based analysis and classification tasks

Support vector machines are used in many tasks when it comes to dealing with images.
SVMs are particularly used in one definite application of image processing: facial features extraction and recognition.

While working with facial features, we need algorithms that can properly classify different features based on very fine-tuned feature extractions. Facial expressions are one of the most versatile features while working with image processing. Like your face can be way different than the face of someone elseโ€™sโ€™. But still, the algorithm should be able to classify and acknowledge the facial expressions given by both of you, even though the model be trained on just your face or the other person.

SVMs work amazingly well because of its ability to create the largest margin possible while dividing different points on the feature maps. The basic algorithm hence can work perfectly when it comes to fitting the model data which is based on the finely accumulated facial features, like expressions.

Geo-spatial data-based applications

One of the fields which has the most uncleaned data is the geoscience field. The geospatial data is one of the noisiest data out there. SVMs still works pretty well, especially when the dataset size is not so high. The most problematic set of geospatial analysis problems are inversion problems. For example, the geo-sounding problem. In this, the electromagnetic data acquired is passed through the SVMs algorithm and it can correctly classify those.

The thing about geospatial data is that not only that the data is noisy, but it is very delicate too. Because of this, the sample points are very close to each other. If we need to classify those sample properly and train a model which is not going to overfit, then you need to use support vector machines.

Text-based applications

We can use support vector machines to classify the handwriting of two different people. SVMs train better when it comes to applications such as detection of the curves and straights used in typical handwriting. SVMs can also be used in pure computer-based texts. For example, a typical text-based classification task is the email spam classifier. In that, we need to classify an email that is spam from the email which is not a spam. It is one of the most used applications in the email delivery systems provided by platforms like Gmail. SVMs can correctly classify the spams from the pool of emails. Some of the SVMs trained on structured data achieve as high as 97 percent accuracy for this application.

When it comes to text-based applications, we are talking about language. Another part of the language exchange is speech-based applications.
Audio-based analysis is also a field in which SVMs offer a solution. We can use many audio-based pre-processing functions and then use the SVMs for classification or just simple speech recognition. Both of these applications are quite useful and widely used.

Computational biology (Medical)

When it comes to the medical sector, AI has always tried to give a solution. The first use of SVMs in the medical field was based on cancer recognition, which was an image-based application. But the algorithm took its flight in the field when it was first used in the protein analysis tasks. We all know that human-based proteins are very delicate structures and are prone to too much noise as well as errors while using the algorithms for recognition. Another field there is the remote homology which uses SVMs to the fullest. This is where the analysis is dependent on how the protein sequences are modeled.

Of course, the use of SVMs does spread over to the detection of various diseases, based on either image data or text data (value-based). But as they were discussed earlier we are not going to repeat the same here again. However, it is important to mention that they are widely used in many fine crafted classification applications, necessary for the medical sector.

Security-based applications

Well, this may surprise you but there are many applications where SVMs are used for basic encryption as well as complex analysis of different materials to see and even break the encryptions and other security measures.
SVMs can also be used to detect the encryption schemas uploaded to the images, to hide them. Yes, images are used to hide the encryption patterns in secretive transmissions. When the resolution of images goes higher, the more difficult it becomes to detect those patterns and cracking the schema. The SVMs are hence useful when it comes to analyzing and getting the small and astutely observed changes and modifications in the images.
This is how the SVMs are used in the security-based applications. This field is still under heavy research to get even more power from the SVMs.

Conclusion

As we can see in this article at OpenGenus, SVMs can be used in many use cases and this is just the tip of the iceberg. The real uses as we said, are endless. It is just a matter of perception and finding the uses for this efficient classification algorithm.