Applications of Support Vector Machine

Support Vector Machines are algorithms or models that utilize the supervised learning approach to machine learning. They commonly solve issues that fall under the broad umbrellas of classification, regression, or outlier detection. Let us see various applications of support vector machine.

Applications of support vector machine:

Algorithms appear to be very mathematical, and unnecessarily complicated while researching machine learning. As a result, it really is critical to establish a foundation by knowing how to apply these methods.We can use Support Vector Machines in a variety of real-world applications, explained below in detail.

1. Image classification:

When used to solve image classification tasks, an SVM dramatically enhances the performance and reliability of the result. For example, they can be used to distinguish between apples and oranges or cats and dogs.

2. Image segmentation:

In image analysis, segmentation is a crucial step. It separates individual elements from a picture for purposes such as identification or descriptions. Although an SVM technically makes this system more efficient, it does so at the cost of decreasing accuracy because SVMs have the problem of being insensitive to noise.

3. Hand-written character recognition:

SVMs can accomplish Information retrieval and categorization of data using labels when there is a large amount of data. Handwriting recognition falls under both of these topics.

4. Text categorization:

SVMs reduce the requirement for feature selection and hence, greatly simplify text categorization. They’re also quite durable, which is a crucial factor to consider while selecting a model.

5. Financial distress prediction:

Finance, unquestionably among the most significant areas of our everyday life, gains from the use of SVMs. SVMs, for example, detect early symptoms of financial trouble and trends that reveal them.

6. Prediction of common diseases:

In clinical data processing, support vector machines are frequently used. Consider the possibility of identifying disease onset based on previous records. SVMs function well with limited samples and a high number of factors because they are data-driven and independent of a model.

7. Sentiment Analysis:

The optimum decision border between vectors in a particular category and vectors outside of it is determined by support vector machines. This bodes well for using it to perform sentiment analysis. Nevertheless, to fully leverage the system’s capabilities, texts must be represented as vectors. Because SVMs aren’t tuned to noise detection, productivity comes at the expense of precision.

8. Geo-spatial applications:

Geospatial input contains a lot of noise and imperfections and is extremely susceptible to mishaps or wrong interpretations. This results in data points that are very close. SVMs assist in effectively adapting the model to these locations while avoiding overfitting.

9. Biomarker/Signature Discovery

Selecting criteria for classification is part of what is called biomarker discovery, that is, identifying cancerous cells versus non-cancerous ones. SVM may prioritize features based on how much weight they carry.

10. Inverse Geo-sounding:

One of the most significant SVM applications for determining the planet’s layered structure is the geo-sounding problem. It necessitates the resolution of a variety of inversion difficulties. In the instance of electromagnetic data, we use a linear function to address the problem, and the SV learning technique creates a model.

Here, we use mathematical programming methods to create the models. However, because the challenge is tiny, the size of the dimension in which the model is produced may be limited as well.

11. Face detection:

It divides the picture into two categories: face and non-face. It comprises n x n pixel training data with a two-class face (+1) and non-face classification (-1). Then it classifies each pixel’s characteristics as face or non-facial. Creates a rectangular border around faces based on pixel intensity and uses the same approach to classify each photo.

12. Texture Classification:

We identify if a surface is smooth by looking at photos of different textures. This SVM application will be quite beneficial. We could develop a very powerful model which takes photographs with a keen digicam and use the data in our algorithm.

We can also assess if a surface is smooth or rough by taking photographs of it. SVM also classifies the surface as smooth or rough using statistical features like contrast, uniformity and entropy.

13. Protein Fold and Remote Homology Detection

SVM is useful in remote homology identification. It all relies on how the protein sequences are simulated in this scenario. To solve the kernel functions, we apply a variety of methods.

The kernel function aids in determining the degree of similarity between various homologous proteins. The fact is that these kernels frequently outperform many SVM-based approaches. Kernel is a component of SVM altogether.
In this approach, SVM has an impact on fields such as bioinformatics.

14. Seismic Liquefaction Potential:

Because of the high accuracy of the results, SVM is regarded as a superb Machine Learning method. We have two issues: SPT (Standard Penetration Test) and CPT (Comprehensive Penetration Test) (Cone Penetration Test). We examine for the presence and absence of liquefaction in both of these tests. We’re going to use field datasets for this.

The datasets with SVM accuracy were 96 and 97 percent accurate, respectively. SVM assists in the development of models for complicated combinations such as soil characteristics and liquefaction potentials.

15. Generalized Predictive Control:

To regulate chaotic dynamics using usable parameters, we employ SVM-based GPC. It performs admirably when it comes to system control. In terms of the target’s local stability, the system follows chaotic dynamics.
Some of its advantages include:

• Allows for the use of tiny parameter algorithms to reroute a chaotic system to the desired location.
• Minimize the time spent having to wait for chaotic systems.
• Improves the model’s performance.

16. Steganography Detection in Digital Images:

We may use SVM to determine if a picture is pure or contaminated. SVMs may also recognise and hide encryption schemes in pictures.The higher the resolution of the image, the more difficult it is to discover trends and unlock the code. As a consequence, SVMs are useful for assessing and acquiring subtle changes and alterations in photographs.

17. Cancer Diagnosis and Prognosis:

Cancer diagnosis is currently one of the most important scientific disciplines of the planet. The investigations are carried out employing a variety of machine learning methodologies. To discover cancer, Google uses image categorization tools.

There are several examples of this. One of the methods is SVM. By executing many models, SVM aids in diagnosis and prognosis. A large amount of data is evaluated in the form of a picture. Millions of databases are available.

We use SVM to train thousands of models to classify cancer as malignant or benign.

18. Speech Recognition:

Speech recognition is the process of detecting and separating words from speech. Acoustic data is also notoriously loud and complex, particularly when several speakers are engaged. Notwithstanding the noise, SVMs can train effectively.

Summary

Once mastered, SVMs are diverse, competent, and simple to use. As a result, they’re a valuable addition to any developer’s toolkit. The article covered a variety of applications of support vector machine, but the following are the important areas:

• SVMs function well with limited samples and a high number of factors because they are data-driven and independent of a model.
• These work great when there is a margin of class separation.
• These are very effective when the number of features in a dataset is greater than the number of observations.