Capterra Glossary
Support Vector Machine (SVM)
A support vector machine (SVM) is a deep learning algorithm often used for data classification and regression analysis. SVMs are supervised machine learning algorithms, which means they have to be trained on how to label data before they can be used to classify data and examine the relationship between data points.
The SVM algorithm focuses on finding a hyperplane (a supervised learning tool that separates and classifies a dataset) that accurately classifies the various points within an existing dataset. SVMs are used in various industries for gene classification, handwriting recognition, facial detection, image classification, and other tasks that use algorithms to classify datasets.
What Small and Midsize Businesses Need to Know About Support Vector Machine (SVM)
Since the SVM algorithm can accurately classify data into two separate categories, small computer forensics laboratories often use it to classify different voices in audio recordings and find similarities between handwriting samples.
Small and midsize digital marketing firms use SVMs for sentiment analysis. SVMs can categorize text generated through social media comments and posts, blogs, and consumer reviews based on whether it is positive or negative. This helps the firms get an insight into public sentiments about their product and service marketing campaigns.
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