Binary Classifier Inspired by Quantum Theory

AAAI 2019 pp. 10051-10052

doi:10.1609/AAAI.V33I01.330110051 /aaai/2019/tiwari2019aaai-binary/

Abstract

Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.

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Cite

Text

Tiwari and Melucci. "Binary Classifier Inspired by Quantum Theory." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.330110051

Markdown

[Tiwari and Melucci. "Binary Classifier Inspired by Quantum Theory." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/tiwari2019aaai-binary/) doi:10.1609/AAAI.V33I01.330110051

BibTeX

@inproceedings{tiwari2019aaai-binary,
  title     = {{Binary Classifier Inspired by Quantum Theory}},
  author    = {Tiwari, Prayag and Melucci, Massimo},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {10051-10052},
  doi       = {10.1609/AAAI.V33I01.330110051},
  url       = {https://mlanthology.org/aaai/2019/tiwari2019aaai-binary/}
}