The purpose of document classification is to assign the most appropriate label to a specified
document. The main challenges in document classification are insufficient label information …

Many machine learning algorithms have been applied to text classification tasks. In the
machine learning paradigm, a general inductive process automatically builds a text classifier …

Supervised text classification methods are efficient when they can learn with reasonably
sized labeled sets. On the other hand, when only a small set of labeled documents is …

Traditionally, text classifiers are built from labeled training examples. Labeling is usually
done manually by human experts (or the users), which is a labor intensive and time …

Developing text classifiers often requires a large number of labeled documents as training
examples. However, manually labeling documents is costly and time-consuming. Recently …

The topic of text document image classification has been explored extensively over the past
few years. Most recent approaches handled this task by jointly learning the visual features of …

We present a feature vector formation technique for documents-Sparse Composite
Document Vector (SCDV)-which overcomes several shortcomings of the current …

Text classification is a pivotal task in NLP (Natural Language Processing), which has
received widespread attention recently. Most of the existing methods leverage the power of …

Text classification aims at mapping documents into a set of predefined categories.
Supervised machine learning models have shown great success in this area but they …

The rapid growth of electronic documents are causing problems like unstructured data that
need more time and effort to search a relevant document. Text Document Classification …