CONCLUSION


The conventional techniques used for pattern matching are designed to solve many types of text searching problems. The technique based on CMM has some limitations, and it is suitable only for some types of text searching problems. CMM is suitable for tasks where we need an efficient search tool in cases where the precision of the answer is not critical. The advantage of CMM is its ability to work with noisy data. The other advantage of the technique is fast processing.

We have shown that the coding of input patterns significantly affects the capacity of correlation matrix memory. Simple "1 of N" coding does not give good results. We have proposed two coding schemes. Both give good results because of their nearly uniform distribution. However, the method of "random shift" does not keep the ability of CMM to deal with corrupt patterns. The speed experiments show promising results when compared to traditional fast techniques, such as Boyer-Moore. The reason is another approach to searching the patterns. The technique based on CMM recalls only the pattern from the associative memory and takes the position of a pattern from the position table. On the other hand, the CMM technique needs to be trained before recalling. Training processes text linearly with the size of the text.

In the future, we want to study and improve the coding of input patterns. Next, we want to apply this technique to the approximate searching problem. We also want to use more advanced architecture (more CMM) to get better results of processing.




(ed.) Intelligent Agents for Data Mining and Information Retrieval
(ed.) Intelligent Agents for Data Mining and Information Retrieval
ISBN: N/A
EAN: N/A
Year: 2004
Pages: 171

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