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Elicitation of Non-Linearity from Expert Drawing

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Peltola, Tomi
dc.contributor.author Chauhan, Rohan
dc.date.accessioned 2019-06-23T15:07:08Z
dc.date.available 2019-06-23T15:07:08Z
dc.date.issued 2019-06-17
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/38960
dc.description.abstract Machine learning methods do not perform very well with little data because there is not enough information to learn. The choice is to either obtain more data or elicit knowledge from an expert. Obtaining more data might be infeasible because of the associated cost or required time. In such cases, we opt for expert knowledge elicitation. Current expert knowledge elicitation methods either query the user for data points or regarding the relevance of parameters. However, there is no method which allows expressing the non-linearity intuitively without requiring knowledge of Bayesian statistics. We propose expert knowledge elicitation through drawing where the expert draws the fit through data points. We then combine the observed data and drawing data to select the right kernel for a Gaussian process. We also conduct a user study for testing the usability of the proposed method. We obtain better performance with the proposed model for kernel selection and extrapolation in comparison to the baseline model using only observed data. en
dc.format.extent 57
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Elicitation of Non-Linearity from Expert Drawing en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword expert knowledge elicitation en
dc.subject.keyword Gaussian process en
dc.subject.keyword drawing en
dc.subject.keyword kernel selection en
dc.identifier.urn URN:NBN:fi:aalto-201906234026
dc.programme.major Machine Learning, Data Science and Artificial Intelligence fi
dc.programme.mcode SCI3044 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Kaski, Samuel
dc.programme Master’s Programme in Computer, Communication and Information Sciences fi
local.aalto.electroniconly yes
local.aalto.openaccess yes

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