Name
Abstract | ||
|---|---|---|
The cognitive framework of conceptual spaces bridges the gap between symbolic and subsymbolic AI by proposing an intermediate conceptual layer where knowledge is represented geometrically. There are two main approaches for obtaining the dimensions of this conceptual similarity space: using similarity ratings from psychological experiments and using machine learning techniques. In this paper, we propose a combination of both approaches by using psychologically derived similarity ratings to constrain the machine learning process. This way, a mapping from stimuli to conceptual spaces can be learned that is both supported by psychological data and allows generalization to unseen stimuli. The results of a first feasibility study support our proposed approach. |
Year | Venue | Field |
|---|---|---|
2018 | AIC | Artificial intelligence,Cognition,Artificial neural network,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1804.07758 | 0 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Bechberger | 1 | 3 | 2.76 |
| Elektra Kypridemou | 2 | 2 | 1.72 |