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| Content Provider | Springer Nature Link |
|---|---|
| Author | Shivhare, Radhika Cherukuri, Aswani Kumar |
| Copyright Year | 2016 |
| Abstract | Human brain represents the information and stores it as memory. They are stored in different parts of the brain and are linked together by associations. When a cue is provided, the memory is recalled through association. Encoding of the real world information is in the form of object-attribute relation. It is possible to perform both positive recall (object having the attribute and attribute shared by object) and negative recalls (object not having the attribute and attribute not shared by object) from memory. It is evident from literature that the formal concept analysis (FCA) based on bidirectional associative memory (BAM) performs only positive recall from memory. In this paper, FCA based on BAM is extended to three-way formal concept analysis (3WFCA) to achieve a more precise recall. In this extended model, both positive recall and negative recall are performed. In order to achieve this objective, an extra operator namely negative operator is added. The proposed model is validated with an experiment on real world scenario. We also presented the connection of the proposal with long term potentiation (LTP) and Hippocampus of the human brain. |
| Starting Page | 21 |
| Ending Page | 34 |
| Page Count | 14 |
| File Format | |
| ISSN | 18688071 |
| Journal | International Journal of Machine Learning and Cybernetics |
| Volume Number | 8 |
| Issue Number | 1 |
| e-ISSN | 1868808X |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2016-09-17 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Memory Cognition Three-way decision Formal concept analysis Three-way formal concept analysis Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Complex Systems Systems Biology Pattern Recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Vision and Pattern Recognition Software |
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