Now showing items 1-4 of 4

  • Case based reasoning as a model for cognitive artificial intelligence. 

    Craw, Susan; Aamodt, Agnar (Springer https://doi.org/10.1007/978-3-030-01081-2_5, 2018-10-09)
    CRAW, S. and AAMODT, A. 2018. Case based reasoning as a model for cognitive artificial intelligence. In Cox, M.T., Funk, P. and Begum, S. (eds.) Lecture notes in computer science, 11156. Case-based reasoning research and development; proceedings of the 26th International conference on case-based reasoning (ICCBR-18), 9-12 July 2018, Stockholm, Sweden. Cham: Springer [online], pages 62-77. Available from: https://doi.org/10.1007/978-3-030-01081-2_5.
    Cognitive Systems understand the world through learning and experience. Case Based Reasoning (CBR) systems naturally capture knowledge as experiences in memory and they are able to learn new experiences to retain in their ...
  • Improving kNN for human activity recognition with privileged learning using translation models. 

    Wijekoon, Anjana; Wiratunga, Nirmalie; Sani, Sadiq; Massie, Stewart; Cooper, Kay (Springer https://doi.org/10.1007/978-3-030-01081-2_30, 2018-10-09)
    WIJEKOON, A., WIRATUNGA, N., SANI, S., MASSIE, S. and COOPER, K. 2018. Improving kNN for human activity recognition with privileged learning using translation models. In Cox, M.T., Funk, P. and Begum, S. (eds.) Lecture notes in computer science, 11156. Case-based reasoning research and development; proceedings of the 26th International conference on case-based reasoning (ICCBR-18), 9-12 July 2018, Stockholm, Sweden. Cham: Springer [online], pages 448-463. Available from: https://doi.org/10.1007/978-3-030-01081-2_30
    Multiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is preferred by consumers as it is more convenient and less intrusive. This presents a ...
  • Neural induction of a lexicon for fast and interpretable stance classification. 

    Clos, Jérémie; Wiratunga, Nirmalie (Springer https://doi.org/10.1007/978-3-319-59888-8_16, 2017-05-27)
    CLOS, J. and WIRATUNGA, N. 2017. Neural induction of a lexicon for fast and interpretable stance classification. Lecture notes in computer science, 10318, Proceedings of the 1st international conference on language, data and knowledge (LDK 2017), 19-20 June 2017, Galway, Ireland. Cham: Springer [online], pages 181-193. Available from: https://dx.doi.org/10.1007/978-3-319-59888-8_16
    Large-scale social media classification faces the following two challenges: algorithms can be hard to adapt to Web-scale data, and the predictions that they provide are difficult for humans to understand. Those two challenges ...
  • Personalised human activity recognition using matching networks. 

    Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Cooper, Kay (Springer https://doi.org/10.1007/978-3-030-01081-2_23, 2018-10-09)
    SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Personalised human activity recognition using matching networks. In Cox, M.T., Funk, P. and Begum, S. (eds.) Lecture notes in computer science, 11156. Case-based reasoning research and development; proceedings of the 26th International conference on case-based reasoning (ICCBR-18), 9-12 July 2018, Stockholm, Sweden. Cham: Springer [online], pages 339-353. Available from: https://doi.org/10.1007/978-3-030-01081-2_23
    Human Activity Recognition (HAR) is typically modelled as a classification task where sensor data associated with activity labels are used to train a classifier to recognise future occurrences of these activities. An ...