Support Vector Machines based on a semantic kernel for text categorization

Georges Siolas; Florence d'Alche-Buc

doi:10.1109/ijcnn.2000.861458

Support Vector Machines based on a semantic kernel for text categorization

Georges Siolas
, Florence d'Alche-Buc

Sorbonne Université

Research output: Contribution to conference › Paper › peer-review

Abstract

We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20 - newsgroups database. Support Vector Machines provide the best accuracy on test data.

Original language	English
Pages	205-209
Number of pages	5
DOIs	https://doi.org/10.1109/ijcnn.2000.861458
Publication status	Published - 1 Jan 2000
Event	International Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy Duration: 24 Jul 2000 → 27 Jul 2000

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'2000)
City	Como, Italy
Period	24/07/00 → 27/07/00

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Cite this

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abstract = "We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20 - newsgroups database. Support Vector Machines provide the best accuracy on test data.",

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AB - We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20 - newsgroups database. Support Vector Machines provide the best accuracy on test data.

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M3 - Paper

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T2 - International Joint Conference on Neural Networks (IJCNN'2000)

Y2 - 24 July 2000 through 27 July 2000

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Support Vector Machines based on a semantic kernel for text categorization

Abstract

Conference

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