Abstract
This paper mainly emphasis on the use of machine learning algorithms such as self-organizing maps (SOM) and support vector machines (SVM) for classifying text documents. We have to classify documents effectively and accurately to different classes based on their content. We tested classification of self-organizing map on Reuters R-8 data set and compared the results to three other popular machine learning algorithms: k-means clustering, k nearest neighbor searching, and Naive Bayes classifier. Self-organizing map yielded the highest accuracies as an unsupervised method. Furthermore, the accuracy of self-organizing maps was improved when used together with support vector machines.
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Patil, V., Jadhav, Y., Sirsat, A. (2021). Categorizing Documents by Support Vector Machine Trained Using Self-Organizing Maps Clustering Approach. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_2
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DOI: https://doi.org/10.1007/978-3-030-69921-5_2
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