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| An associative search methodology is presented whereby words, phrases, entire documents or technical jargon are input to the system for the purposes of searching a database for like items. A self-organizing associative data structure utilizing Latent Semantic Analysis (LSA) combined with a Hierarchical Mixture of Experts (HME) neural network learning system tracks relationships between words and word groupings as well as user significance feedback to automatically expand the search so that data elements can be found which cross technological/taxonomic boundaries. This results in an increase in both recall and precision parameters for each individual user's search.first preferred embodiment present user inputs search parameter word phrase database documents Latent Semantic Analysis (LSA) retrieve relevant prioritizes based Hierarchical Mixture Experts (HME) neural network records viewed feeds information learn significant particular FIG subjected batch mode analysis matrix representing conceptual space initiate enters interface term results point n-dimensional represents distance points compared arbitrary metric fall returned unordered list 10 |
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