A nonparametric classifier for unsegmented text

Symbolic Indirect Correlation (SIC) is a new classification method for unsegmented patterns. SIC requires two levels of comparisons. First, the feature sequences from an unknown query signal and a known multi-pattern reference signal are matched. Then, the order of the matched features is compared with the order of matches between every lexicon symbolstring and the reference string in the lexical domain. The query is classified according to the best matching lexicon string in the second comparison. Accuracy increases as classified feature-and-symbol strings are added to the reference string.

• Type: Conference
• Year: 2004
• Where: DRR
• Authors: George Nagy, Ashutosh Joshi, Mukkai S. Krishnamoorthy, Yu Lin, Daniel P. Lopresti, Shashank K. Mehta, Sharad C. Seth
• Keywords: DRR 2004, DRR 2010, Multi-pattern Reference Signal, Reference String, Symbolic Indirect Correlation