Detecting and quantifying ambiguity: a neural network approach

Author:

Ligeiro Rui,Vilela Mendes R.ORCID

Funder

Fundação para a Ciência e a Tecnologia (PT)

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Reference43 articles.

1. Albalate A, Suchindranath A, Soenmez MM, Suendermann D (2010) On ambiguity detection and postprocessing schemes using cluster ensembles, ICAART 2010. In: Proceedings of the international conference on agents and artificial intelligence. INSTICC Press, pp 623–630

2. Alfaro-Ponce M, Cruz AA, Chairez I (2014) Adaptive identifier for uncertain complex nonlinear systems based on continuous neural networks. IEEE Trans Neural Netw Learn Syst 25:483–494

3. Alippi C (1999) FPE-based criteria to dimension feedforward neural topologies. IEEE Trans Circuits Syst I 46:962–973

4. Alippi C, Piuri V, Sami M (1995) Sensitivity to errors in artificial neural networks: a behavioural approach. IEEE Trans Circuits Syst I 42:358–361

5. Bailey-Kellogg C, Ramakrishnan N (2001) Ambiguity-directed sampling for qualitative analysis of sparse data from spatially-distributed physical systems. In: Proceedings of the IJCAI

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