By using machine learning algorithms, documents can be assigned into different categories, titles, languages or even emotional conditions. In this study we describe our work on creating a …

Apr 27, 2015 · The goal of this note is to point out that any distributed representation can be turned into a classifier through inversion via Bayes rule. The approach is simple and modular, …

Jan 1, 2015 · In this paper, we propose a supervised framework (Compound RNN) for document classification based on document-level distributed representations learned from deep learning …

Jun 12, 2016 · In the paper we show how these consecutive steps can be realized on the Apache Spark platform dedicated to distributed processing of big data. We illustrate the proposed …

We are proposing a simple alternative that turns tted distributed lan- guage representations into document classiers 45. without any additional modeling or estimation.

Document classification is the primary process of retrieving, filtering, clustering and extracting documents. A general procedure for document classification is as follows: First, a set of pre …

In this paper, we propose a new document classification method combining BOW and multiple distributed representations. Different corpus has different words and phrases, so each …

Feb 10, 2018 · We use Apache Big Data technologies such as Hadoop, HDFS, Spark and Mahout, and apply these distributed technologies to Machine Learning. Subjects: Information …

We present a distributed documents classification technique using Multi-Agent technology. Naive Bays Classifier is used in a distributed environment for document classification. Experimental …

Feb 10, 2018 · Automated text classification has been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously …