6. (SVM) Support Vector Machine in R language

World Applications of SVM

SVM relies on supervised learning algorithms to perform classifications. It is a powerful method to classify unstructured data, make reliable predictions, and reduce redundant information. 

What’s more, SVM has applications in different areas of daily life, such as: 

  • Face Detection Using image training data, SVM classifies pixels in images like a face or non-face
  • Text ClassificationTraining data is used to categorize different types of documents. For instance, news articles can be classified as “business” or “entertainment.” 
  • Classifying ImagesBy classifying images with improved techniques, SVM increases search accuracy. 
  • BioinformaticsSVM algorithms have increased the effectiveness in protein homology detection, cancer classification, gene classification, and more. 

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