The words in a document are extracted and the frequencies of occurrences of these words are measured. The frequencies may be modified in several complex ways depending on the algorithm. The proximity of documents is generally determined based on the collective proximity of word frequencies. As with Internet search engines, the frequency-based approach can often lead to mismatches. The approach used in NCF differs from frequency-based approaches.
Classification is done using adaptive resonance neural networks.
These neural networks are known to have properties that model human classification. Therefore unlike other methods, NCF filters can be incrementally refined by user feedback. NCF training is done offline, online classification does not involve comparisons between documents but simply a lookup in a compressed table. In the training phase, a large collection of representative documents is processed to determine a table of document categories. In the classification phase, new documents are processed using the table that was created during the categorization phase.