Crowd Sourcing as an Improvement of N-Grams Text Document Classification Algorithm

2020 ◽  
Author(s):  
Petr Saloun ◽  
David Andrsic ◽  
Barbora Cigankova ◽  
Ioannis Anagnostopoulos
Keyword(s):  
Document Classification ◽  
Classification Algorithm ◽  
Crowd Sourcing ◽  
Text Document ◽  
Text Document Classification

Hybrid Neural Architecture for Intelligent Recommender System Classification Unit Design

2013 ◽  
pp. 192-213
Author(s):  
Emmanuel Buabin
Keyword(s):  
Recommender System ◽  
Document Classification ◽  
Research Field ◽  
Neural Systems ◽  
Fully Integrated ◽  
Text Document ◽  
Unit Design ◽  
Boosting Algorithms ◽  
New Research ◽  
Text Document Classification

The objective is intelligent recommender system classification unit design using hybrid neural techniques. In particular, a neuroscience-based hybrid neural by Buabin (2011a) is introduced, explained, and examined for its potential in real world text document classification on the modapte version of the Reuters news text corpus. The so described neuroscience model (termed Hy-RNC) is fully integrated with a novel boosting algorithm to augment text document classification purposes. Hy-RNC outperforms existing works and opens up an entirely new research field in the area of machine learning. The main contribution of this book chapter is the provision of a step-by-step approach to modeling the hybrid system using underlying concepts such as boosting algorithms, recurrent neural networks, and hybrid neural systems. Results attained in the experiments show impressive performance by the hybrid neural classifier even with a minimal number of neurons in constituting structures.

Hindi Text Document Classification System Using SVM and Fuzzy

2018 ◽  
Vol 5 (4) ◽  
pp. 1-31 ◽  
Author(s):  
Shalini Puri ◽  
Satya Prakash Singh
Keyword(s):  
Classification System ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Document Classification ◽  
Data Availability ◽  
Support Vector ◽  
Handwritten Documents ◽  
Text Document ◽  
Survey Report ◽  
Text Document Classification

In recent years, many information retrieval, character recognition, and feature extraction methodologies in Devanagari and especially in Hindi have been proposed for different domain areas. Due to enormous scanned data availability and to provide an advanced improvement of existing Hindi automated systems beyond optical character recognition, a new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic is introduced. This first pre-processes and then classifies textual imaged documents into predefined categories. With this concept, this article depicts a feasibility study of such systems with the relevance of Hindi, a survey report of statistical measurements of Hindi keywords obtained from different sources, and the inherent challenges found in printed and handwritten documents. The technical reviews are provided and graphically represented to compare many parameters and estimate contents, forms and classifiers used in various existing techniques.

Text Document Classification Using Support Vector Machine with Feature Selection Using Singular Value Decomposition

2014 ◽  
Vol 905 ◽  
pp. 528-532
Author(s):  
Hoan Manh Dau ◽  
Ning Xu
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Decision Tree ◽  
Document Classification ◽  
Singular Value ◽  
Multidimensional Data ◽  
Support Vector ◽  
Text Document ◽  
Text Document Classification ◽  
Value Decomposition

Text document classification is content analysis task of the text document and then giving decision (or giving a prediction) whether this text document belongs to which group among given text document ones. There are many classification techniques such as decision method basing on Naive Bayer, decision tree, k-Nearest neighbor (KNN), neural network, Support Vector Machine (SVM) method. Among those techniques, SVM is considered the popular and powerful one, especially, it is suitable to huge and multidimensional data classification. Text document classification with characteristics of very huge dimensional numbers and selecting features before classifying impact the classification results. Support Vector Machine is a very effective method in this field. This article studies Support Vector Machine and applies it in the problem of text document classification. The study shows that Support Vector Machine method with choosing features by singular value decomposition (SVD) method is better than other methods and decision tree.

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