Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The paper investigates SNN-tree algorithm that extends the binary search tree algorithm so that it can deal with distorted input vectors. Unlike the SNN-tree algorithm, popular methods (LSH, k-d tree, BBF-tree, spill-tree) stop working as the dimensionality of the space grows (N > 1000). The proposed algorithm works much faster than exhaustive search (26 times faster at N=10000). Hover, some errors may occur during the search.   managed to obtain an estimate of the upper bound on the error probability for SNN-tree algorithm. In case when the dimensionality of inputs vectors is N≥500 bits, the probability of error is so small (P<10-15) that it can be neglected according to this estimate and experimental results. In fact,  can consider the proposed SNN-tree algorithm to be exact for high dimensionality (N ≥ 500).
['Vladimir Kryzhanovsky', 'Magomed Malsagov', 'Irina Zhelavskaya']
I am writing a research paper on a idea Error probability for scalar neural network trees in high-dimensional binary space
[{"from": "human", "value": "I am writing a research paper on a idea Error probability for scalar neural network trees in high-dimensional binary space"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The paper investigates SNN-tree algorithm that extends the binary search tree algorithm so that it can deal with distorted input vectors. Unlike the SNN-tree algorithm, popular methods (LSH, k-d tree, BBF-tree, spill-tree) stop working as the dimensionality of the space grows (N > 1000). The proposed algorithm works much faster than exhaustive search (26 times faster at N=10000). Hover, some errors may occur during the search.   managed to obtain an estimate of the upper bound on the error probability for SNN-tree algorithm. In case when the dimensionality of inputs vectors is N\u2265500 bits, the probability of error is so small (P<10-15) that it can be neglected according to this estimate and experimental results. In fact,  can consider the proposed SNN-tree algorithm to be exact for high dimensionality (N \u2265 500)."}]