Learning the Unknown A Computational Approach to Incrementally Developing a Hypothesis Space with Gaussian Mixture Models

Simple item page
dc.contributor.advisorKwisthout, Johan
dc.contributor.authorWol, Erwin de
dc.date.issued2020-06-02
dc.description.abstractWe present a new computational model that captures a key behaviour found in humans we wish to replicate, namely the ignoring of singular anomalous stimuli. Our model is built around incremental learning in Gaussian mixture models combined with an explicit `unknown' hypothesis with posterior reasoning. We show through simula- tions that our model indeed captures the behaviour of interest as opposed to IGMM, the model it was based on. We also show that our model improves the performance of IGMM, providing theoretical evidence that ignoring anomalies is a superior method of learning as opposed to tting all the data.en_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/10606
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleLearning the Unknown A Computational Approach to Incrementally Developing a Hypothesis Space with Gaussian Mixture Modelsen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
S4244907_WolffE.pdf
Size:
524.84 KB
Format:
Adobe Portable Document Format
Download
Collections
Faculteit der Sociale Wetenschappen