Applications of SVM in Machine Learning
SVM stands for Support Vector Machine and is a supervised machine learning method. It solves classification, regression, and outlier detection problems. While studying machine learning, algorithms tend to appear very mathematical. Therefore it is important to ground ourselves by learning what these algorithms are used for. Let us see the Applications of SVM.
Real-Life SVM Applications
Support Vector Machines are used in a variety of real-world applications, explained below in detail.
1. Image classification:
Applying an SVM to image classification problems improves the accuracy and efficiency of the solution significantly.
Example: distinguishing between diseased leaves and healthy ones.
2. Image segmentation:
Segmentation is an important stage in image processing.It extracts specific objects from an image like description or recognition. Theoretically, an SVM makes this process more efficient, however, the efficiency comes at a cost of accuracy because SVMs are not very sensitive to noise.
3. Hand-written character recognition:
Information retrieval and categorization of data using labels can be accomplished using SVMs when there is a large amount of data.Handwriting recognition falls under both of these topics.
4. Text categorization:
SVMs eliminate the need for feature selection and make text categorization significantly easier. They are also very robust, which is an important consideration when choosing an algorithm.
5. Financial distress prediction:
Undeniably one of the most important fields, Finance also benefits from the application of SVMs. For example, SVMs recognise the early-warning of financial distress and patterns that expose the same.
6. Prediction of common diseases:
Support vector machines are very commonly used in medical data analysis. Consider for example, predicting the onset of diseases from historical data. SVMs have a data-driven and model-free approach which works great with small sample sizes and a large number of variables.
7. Sentiment Analysis:
Support vector machines determine the best decision boundary between vectors in a given category and vectors outside of it. This bodes well for applying it to sentiment analysis tasks.However, it is necessary to represent texts as vectors in order to utilize maximum capacity of the algorithm.
8. Encryption:
SVMs may also be used to recognize and hide encryption schemes that have been added to pictures. The higher the picture resolution, the harder it is to spot patterns and crack the schema. As a result, SVMs are beneficial for evaluating and obtaining minor alterations and modifications in pictures.
9. Geo-spatial applications:
Geographical data is noisy and exceedingly fragile. This tends to generate sample points that are quite near to one another. SVMs are employed to accurately fit model to these points while also preventing overfitting
10. Biomarker/Signature Discovery
The process of biomarker discovery may be thought of as a selection of characteristics for categorization (cancer versus non-cancer).SVM is applied to remove the features with least weight and select the features with most weight as most important.
11. Speech Recognition:
Speech recognition detects and separates words out of continuous speech. Acoustic data also tends to be very noisy and has multiple factors, especially when multiple speakers are involved.SVMs are used because they are able to train efficiently despite the noise.
Summary
SVMs are versatile, capable and easy to apply once understood. This makes them a powerful tool in any programmer’s toolbox. We’ve seen various applications of SVM but here are the underlying points:
- SVMs have a data-driven and model-free approach which works great with small sample sizes and a large number of variables.
- 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