Alternative approach to pattern recognition is discussed that amounts to operations on inverse patterns and resembles working of Google-type search engines. Unlike neural networks that iteratively calculate weights within many a learning cycles, inverse patterns-based paradigm (neural cortex) does not use weights and follows a challenging learning trend that attempts to achieve a human-like generalization from a single example.
Inverted files emerged in computer science as information indexing tools for large-scale search applications. After decades of computing, only one general method has been found able to deal quickly and efficiently with vast amounts of data. That is indexing, which is at the heart of both Google search and large scale DNA processing. However, indexing-based pattern recognition is virtually non-existent. The paper provides a mathematical framework that unifies search and pattern recognition algorithms.
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