Title :
SVM-based Hypertext Information Categorization
Abstract :
An algorithm of hypertext information categorization based on SVM was introduced. The algorithm divided a large data-set into many non-intersecting subsets during training period, in which the samples are trained according to the batch and then many classifications are constructed. The classifications are optimized by error correcting output codes (ECOC), which reduces the amount of documents to be studied in the deep level training phase. The application of this algorithm to hypertext information categorization underlines the theoretical results.
Keywords :
document handling; error correction codes; hypermedia; pattern classification; support vector machines; ECOC; SVM-based hypertext information categorization; classifications; deep level training phase; documents; error correcting output codes; large data-set; nonintersecting subsets; training period; Industrial control; Categorization; Error Correcting Output Codes; Multi-level classification; Support Vector Machine;
Conference_Titel :
Industrial Control and Electronics Engineering (ICICEE), 2012 International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4673-1450-3
DOI :
10.1109/ICICEE.2012.358
