Victoria Hodge | University of York (original) (raw)
Papers by Victoria Hodge
Frontiers in artificial intelligence, Nov 19, 2020
Journal of The Audio Engineering Society, Mar 10, 2019
Machine learning with applications, Jun 1, 2023
IGI Global eBooks, Oct 14, 2022
In this paper we evaluate a selection of data retrieval algorithms for storage efficiency, retrie... more In this paper we evaluate a selection of data retrieval algorithms for storage efficiency, retrieval speed and partial matching capabilities using a large Information Retrieval dataset. We evaluate standard data structures, for example inverted file lists and hash tables, but also a novel binary neural network that incorporates: single-epoch training, superimposed coding and associative matching in a binary matrix data structure. We identify the strengths and weaknesses of the approaches. From our evaluation, the novel neural network approach is superior with respect to training speed and partial match retrieval time. From the results, we make recommendations for the appropriate usage of the novel neural approach.
IEEE Transactions on Intelligent Transportation Systems, Jun 1, 2015
Neurocomputing, Oct 1, 2006
arXiv (Cornell University), May 21, 2019
Neural Networks, Jun 1, 2016
IEEE Transactions on Knowledge and Data Engineering, Sep 1, 2003
arXiv (Cornell University), May 2, 2018
Frontiers in artificial intelligence, Nov 19, 2020
Journal of The Audio Engineering Society, Mar 10, 2019
Machine learning with applications, Jun 1, 2023
IGI Global eBooks, Oct 14, 2022
In this paper we evaluate a selection of data retrieval algorithms for storage efficiency, retrie... more In this paper we evaluate a selection of data retrieval algorithms for storage efficiency, retrieval speed and partial matching capabilities using a large Information Retrieval dataset. We evaluate standard data structures, for example inverted file lists and hash tables, but also a novel binary neural network that incorporates: single-epoch training, superimposed coding and associative matching in a binary matrix data structure. We identify the strengths and weaknesses of the approaches. From our evaluation, the novel neural network approach is superior with respect to training speed and partial match retrieval time. From the results, we make recommendations for the appropriate usage of the novel neural approach.
IEEE Transactions on Intelligent Transportation Systems, Jun 1, 2015
Neurocomputing, Oct 1, 2006
arXiv (Cornell University), May 21, 2019
Neural Networks, Jun 1, 2016
IEEE Transactions on Knowledge and Data Engineering, Sep 1, 2003
arXiv (Cornell University), May 2, 2018