Increase of the Speed of Operation of Scalar Neural Network Tree when Solving the Nearest Neighbor Search Problem in Binary Space of Large Dimension

摘要

In the binary space of large dimension we analyze the nearest neighbor search problem where the required point is a distorted version of one of the patterns. Previously it was shown that the only algorithms able to solve the set problem are the exhaustive search and the neural network search tree. For the given problem the speed of operation of the last algorithm is dozens of times larger comparing with the exhaustive search. Moreover, in the case of large dimensions the neural network tree can be regarded as an accurate algorithm since the probability of its error is so small that cannot be measured. In the present publication, we propose a modification of the scalar neural network tree allowing the speeding of the algorithm's operation up to hundred times without losses in its reliability.