Shatkay et al. [14] combined information from images as well as captions to enhance a text categorization algorithm.
We designed a text categorization algorithm and a filtering user interface to address the problem.
Specifically, cross-lingual text categorization deals with learning a text categorization model from a set of training documents written in one language (e.g., L1) and then classifying new documents in a different language (e.g., L2).
Text categorization pertains to the automatic learning of a text categorization model from a training set of preclassified documents on the basis of their contents and the subsequent assignment of unclassified documents to appropriate categories.
They proposed a text-categorization technique that is able to identify subjective content using minimum cut.
In computer science, the task that we focus on is known as Automatic Text Categorization (ATC) (19).
Task A (triage) relies on information retrieval, sometimes known as text categorization.
As a widely used machine learning algorithm, SVM is based on the idea of structural risk minimization (SRM) and it has successfully been applied to a number of real-world problems, such as face recognition, text categorization, and object detection in machine vision.
This, in effect, expands the application of transfer learning to many other real-world tasks such as cross-language text categorization, text-to-image classification, and many others.
Since SVM is able to handle large feature spaces, it has been successfully used in solving many real world problems such as text categorization, image classification, protein analysis, cancer data classification, and hand-writing recognition [ 39].
