Text and Hypertext Categorization
Image Classification
Clustering of News Articles
All of the above
Answer is Right!
Answer is Wrong!
The correct answer is D. All of the above.
Support vector machines (SVMs) are a type of supervised machine learning algorithm that can be used for classification and regression tasks. SVMs are known for their ability to generalize well to unseen data, and they have been successfully applied to a wide range of real-world problems.
Some of the real-world applications of SVMs include:
- Text and Hypertext Categorization: SVMs can be used to classify text documents into different categories. For example, SVMs can be used to classify emails into spam or non-spam, or to classify news articles into different topics.
- Image Classification: SVMs can be used to classify images into different categories. For example, SVMs can be used to classify images of cats and dogs, or to classify images of different types of flowers.
- Clustering of News Articles: SVMs can be used to cluster news articles into different groups. For example, SVMs can be used to cluster news articles by topic, or to cluster news articles by author.
In addition to these applications, SVMs have also been used for a variety of other tasks, such as face recognition, speech recognition, and web search.
SVMs are a powerful tool for machine learning, and they have been successfully applied to a wide range of real-world problems.