Real-Life Applications of SVM (Support Vector Machines)
In our previous Machine Learning blog, we have discussed the detailed introduction of SVM(Support Vector Machines). Now we are going to cover the real life applications of SVM such as face detection, handwriting recognition, image classification, Bioinformatics etc.
2. Applications of SVM in Real World
As we have seen, SVMs depends on supervised learning algorithms. The aim of using SVM is to correctly classify unseen data. SVMs have a number of applications in several fields.
Some common applications of SVM are-
- Face detection – SVMc classify parts of the image as a face and non-face and create a square boundary around the face.
- Text and hypertext categorization – SVMs allow Text and hypertext categorization for both inductive and transductive models. They use training data to classify documents into different categories. It categorizes on the basis of the score generated and then compares with the threshold value.
- Classification of images – Use of SVMs provides better search accuracy for image classification. It provides better accuracy in comparison to the traditional query-based searching techniques.
- Bioinformatics – It includes protein classification and cancer classification. We use SVM for identifying the classification of genes, patients on the basis of genes and other biological problems.
- Protein fold and remote homology detection – Apply SVM algorithms for protein remote homology detection.
- Handwriting recognition – We use SVMs to recognize handwritten characters used widely.
- Generalized predictive control(GPC) – Use SVM based GPC to control chaotic dynamics with useful parameters.
Let us now see the above applications of SVM in detail-
2.1. Face Detection
It classifies the parts of the image as face and non-face. It contains training data of n x n pixels with a two-class face (+1) and non-face (-1). Then it extracts features from each pixel as face or non-face. Creates a square boundary around faces on the basis of pixel brightness and classifies each image by using the same process.
2.2. Text and Hypertext Categorization
Allows text and hypertext categorization for both types of models; inductive and transductive. It Uses training data to classify documents into different categories such as news articles, e-mails, and web pages
- Classification of news articles into “business” and “Movies”
- Classification of web pages into personal home pages and others
For each document, calculate a score and compare it with a predefined threshold value. When the score of a document surpasses threshold value, then the document is classified into a definite category. If it does not surpass threshold value then consider it as a general document.
Classify new instances by computing score for each document and comparing it with the learned threshold.
2.3. Classification of Images
SVMs can classify images with higher search accuracy. Its accuracy is higher than traditional query-based refinement schemes
In the field of computational biology, the protein remote homology detection is a common problem. The most effective method to solve this problem is using SVM. In last few years, SVM algorithms have been extensively applied for protein remote homology detection. These algorithms have been widely used for identifying among biological sequences. For example classification of genes, patients on the basis of their genes, and many other biological problems.
2.5. Protein Fold and Remote Homology Detection
Protein remote homology detection is a key problem in computational biology. Supervised learning algorithms on SVMs are one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences modeled. The method used to compute the kernel function between them.
2.6. Handwriting Recognition
We can also use SVMs to recognize hand-written characters that use for data entry and validating signatures on documents.
2.7. Geo and Environmental Sciences
We use SVMs for geo (spatial) and spatiotemporal environmental data analysis and modeling series.
2.8. Generalized Predictive Control
We use SVM-based GPC to control chaotic dynamics with useful parameters. It provides excellent performance in controlling the systems. The system follows chaotic dynamics with respect to the local stabilization of the target.
Using SVMs for controlling chaotic systems has the following advantages-
- Allows use of relatively small parameter algorithms to redirect a chaotic system to the target.
- Reduces waiting time for chaotic systems.
- Maintains the performance of systems.
Thus, we conclude that the SVMs can not only make the reliable prediction but also can reduce redundant information. The SVMs also obtained results comparable with those obtained by other approaches.
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