Noise reduction in a statistical approach to text categorization
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Using LSI for text classification in the presence of background text
Proceedings of the tenth international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Reduction for Neural Network Based Text Categorization
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
A Simple KNN Algorithm for Text Categorization
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast and accurate text classification via multiple linear discriminant projections
The VLDB Journal — The International Journal on Very Large Data Bases
A fuzzy approach to classification of text documents
Journal of Computer Science and Technology
Enhancing Text Classification Using Synopses Extraction
WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Supervised Latent Semantic Indexing for Document Categorization
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Deterministic convergence of an online gradient method for BP neural networks
IEEE Transactions on Neural Networks
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
Projected-prototype based classifier for text categorization
Knowledge-Based Systems
Text analysis for detecting terrorism-related articles on the web
Journal of Network and Computer Applications
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New text categorization models using back-propagation neural network (BPNN) and modified back-propagation neural network (MBPNN) are proposed. An efficient feature selection method is used to reduce the dimensionality as well as improve the performance. The basic BPNN learning algorithm has the drawback of slow training speed, so we modify the basic BPNN learning algorithm to accelerate the training speed. The categorization accuracy also has been improved consequently. Traditional word-matching based text categorization system uses vector space model (VSM) to represent the document. However, it needs a high dimensional space to represent the document, and does not take into account the semantic relationship between terms, which can also lead to poor classification accuracy. Latent semantic analysis (LSA) can overcome the problems caused by using statistically derived conceptual indices instead of individual words. It constructs a conceptual vector space in which each term or document is represented as a vector in the space. It not only greatly reduces the dimensionality but also discovers the important associative relationship between terms. We test our categorization models on 20-newsgroup data set, experimental results show that the models using MBPNN outperform than the basic BPNN. And the application of LSA for our system can lead to dramatic dimensionality reduction while achieving good classification results.