Using latent semantic indexing for information filtering
COCS '90 Proceedings of the ACM SIGOIS and IEEE CS TC-OA conference on Office information systems
The nature of statistical learning theory
The nature of statistical learning theory
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Journal of Intelligent Information Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Information Processing and Management: an International Journal
An automatically constructed thesaurus for neural network based document categorization
Expert Systems with Applications: An International Journal
Symbolic representation of text documents
Proceedings of the Third Annual ACM Bangalore Conference
Patent classification system using a new hybrid genetic algorithm support vector machine
Applied Soft Computing
Hybrid robust support vector machines for regression with outliers
Applied Soft Computing
Application of data mining in multi-geological-factor analysis
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Expert Systems with Applications: An International Journal
Measuring financial risk with generalized asymmetric least squares regression
Applied Soft Computing
An improved plagiarism detection scheme based on semantic role labeling
Applied Soft Computing
Evaluation of normalization techniques in text classification for portuguese
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Adaptive SVM-Based classification systems based on the improved endocrine-based PSO algorithm
AMT'12 Proceedings of the 8th international conference on Active Media Technology
The Effect of Stemming on Arabic Text Classification: An Empirical Study
International Journal of Information Retrieval Research
A tensor factorization based least squares support tensor machine for classification
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Future Generation Computer Systems
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This paper presents a least square support vector machine (LS-SVM) that performs text classification of noisy document titles according to different predetermined categories. The system's potential is demonstrated with a corpus of 91,229 words from University of Denver's Penrose Library catalogue. The classification accuracy of the proposed LS-SVM based system is found to be over 99.9%. The final classifier is an LS-SVM array with Gaussian radial basis function (GRBF) kernel, which uses the coefficients generated by the latent semantic indexing algorithm for classification of the text titles. These coefficients are also used to generate the confidence factors for the inference engine that present the final decision of the entire classifier. The system is also compared with a K-nearest neighbor (KNN) and Naive Bayes (NB) classifier and the comparison clearly claims that the proposed LS-SVM based architecture outperforms the KNN and NB based system. The comparison between the conventional linear SVM based classifiers and neural network based classifying agents shows that the LS-SVM with LSI based classifying agents improves text categorization performance significantly and holds a lot of potential for developing robust learning based agents for text classification.