Evolving rules for document classification

TL;DR: This paper considers three publicly-available spam corpora and makes comparison with both support vector machines and naive Bayes classifiers, finding that cost-sensitive multiobjective genetic programming gives the best results across a range of performance measures.

TL;DR: From experimental results, the proposed transductive transfer learning GP system can evolve features from source domains to effectively apply to target domains which are similar to the source domains.

TL;DR: A novel hybrid approach to text classification that integrates a machine learning algorithm along with DistilBERT, a pre-trained deep learning framework for natural language processing, offers a base model fine-tuned on Indian Banking News-Events with a rule-based method that is used by filtering false positives and dealing with false negatives to enhance the results given by the previous classifier.

TL;DR: Three different bi-gram techniques have been used to calculate the similarity between two or more Arabic documents which take the form of course descriptions, indicating that the first technique has demonstrated better performance than the other two techniques as viewed with respect to the human judgment.

TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.

TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.

Abstract: The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference. Bradford Books imprint

TL;DR: This survey discusses the main approaches to text categorization that fall within the machine learning paradigm and discusses in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.