@article{2968,
  author = {Dmitry V Vinogradov},
  title = {FCA-based Approach to Machine Learning},
  journal = {Signals and Telecommunication Journal},
  year = {2020},
  volume = {9},
  number = {1},
  doi = {https://doi.org/10.6025/stj/2020/9/25-30},
  url = {http://www.dline.info/stj/fulltext/v9n1/stjv9n1_3.pdf},
  abstract = {The main result of the paper provides a lower bound on sufficient number of randomly generated formal concepts to correctly predict all important positive test examples with given confidence level. The technique coincides with modern approach to the famous theorem of V.N. Vapnik and A.Ya. Chervonenkis. However the situation is dual to the classical one: in our case test examples correspond to fixed subsets and probabilistically generated formal concepts must fall into selected areas of sufficient large volume.},
}