Dit proefschrift bestaat uit twee delen. In het eerste deel beschrijven we hoe de op prototypen gebaseerde classificator LVQ uitgebreid kan worden door gebruik te maken van maten uit de informatie theorie. Daarnaast vergelijken we verschillende manieren van datarepresentatie in deze LVQ configuratie, in dit geval histogrammen van foto’s, SIFT- en SURF-kenmerken. We tonen hoe hiervoor een enkele gecombineerde afstandsmaat kan worden geformuleerd, door de afzonderlijke afstandsmaten samen te nemen. In het tweede deel onderzoeken we het vinden van causale verbanden en toepassingen op problemen die uit het leven zijn gegrepen. Daarnaast verkennen we de combinatie met relevantie leren in LVQ en tonen we enkele toepassingen

Mwebaze, Ernest

Dit proefschrift bestaat uit twee delen. In het eerste deel beschrijven we hoe de op prototypen gebaseerde classificator LVQ uitgebreid kan worden door gebruik te maken van maten uit de informatie theorie. Daarnaast vergelijken we verschillende manieren van datarepresentatie in deze LVQ configuratie, in dit geval histogrammen van foto’s, SIFT- en SURF-kenmerken. We tonen hoe hiervoor een enkele gecombineerde afstandsmaat kan worden geformuleerd, door de afzonderlijke afstandsmaten samen te nemen. In het tweede deel onderzoeken we het vinden van causale verbanden en toepassingen op problemen die uit het leven zijn gegrepen. Daarnaast verkennen we de combinatie met relevantie leren in LVQ en tonen we enkele toepassingen.This thesis is a two-part thesis. In the first part we discuss extensions of LVQ prototype-based classifiers that use information theoretic measures as distance measures. We also present work on the use of different representations of data, in this case histograms of images, SIFT and SURF features in the same LVQ system. We show the possibility of formulating one combined distance measure for the heterogeneous dataset formed by a combination of their individual representative distance measures. In the second part we delve into causal structure discovery and its application to real world problems. We also presented a first attempt at leveraging some of the techniques of causal learning and applying them to feature relevance learning in LVQ. We also present some deployment examples of some of the techniques to solve real world problems in Uganda particularly using causal analysis to predict the state of food security at the household level and automated diagnosis of crop disease using divergence-based LVQ techniques on a mobile device

Dissertations of the University of Groningen

   University of GroningenDivergences for prototype-based classification and causal structure discoveryMwebaze, ErnestIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2014Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mwebaze, E. (2014). Divergences for prototype-based classification and causal structure discovery: Theoryand application to natural datasets. [Thesis fully internal (DIV), University of Groningen]. [S.n.].CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 31-10-2023Stellingenbehorende bij het proefschriftDivergences for Prototype-based Classification andCausal Structure Discovery,Theory and Application to Natural DatasetsvanErnest Mwebaze1. Divergences can be used in prototype-based classification when dealing withdistributions and for potentially normalised data.2. Inferring underlying causal mechanisms in data is key to understanding theeffects of manipulations or interventions on the data.3. Under certain specific assumptions, it is possible to infer causality frompurely observational data.4. Successful application of machine learning algorithms to real problems shouldbe a significant way of validating the algorithms.5. Views related to this work may be divergent - this work is about divergencesafter all.6. There is an inevitable divergence between the world as it is and the worldas men perceive it.- J.William Fulbright7. Don’t forget that on earth, every big thing has had a small beginning. Whatis born big is monstrous and dies.8. The greatest enemy of the rock is not the pick axe but the small drop ofwater.- Josemaria Escriva9. The object of the life is not to be close to the prototype of the majority, but toescape finding oneself in the cluster of the insane.- Marcus Aurelius, paraphrased10. Ende gut, alles gut. All is well that ends well.

English

Divergences for prototype-based classification and causal structure discovery:Theory and application to natural datasets

Proceedings - University of Groningen

   University of GroningenDivergences for prototype-based classification and causal structure discoveryMwebaze, ErnestIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2014Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mwebaze, E. (2014). Divergences for prototype-based classification and causal structure discovery: Theoryand application to natural datasets. [Thesis fully internal (DIV), University of Groningen]. [S.n.].CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 09-09-2023Stellingenbehorende bij het proefschriftDivergences for Prototype-based Classification andCausal Structure Discovery,Theory and Application to Natural DatasetsvanErnest Mwebaze1. Divergences can be used in prototype-based classification when dealing withdistributions and for potentially normalised data.2. Inferring underlying causal mechanisms in data is key to understanding theeffects of manipulations or interventions on the data.3. Under certain specific assumptions, it is possible to infer causality frompurely observational data.4. Successful application of machine learning algorithms to real problems shouldbe a significant way of validating the algorithms.5. Views related to this work may be divergent - this work is about divergencesafter all.6. There is an inevitable divergence between the world as it is and the worldas men perceive it.- J.William Fulbright7. Don’t forget that on earth, every big thing has had a small beginning. Whatis born big is monstrous and dies.8. The greatest enemy of the rock is not the pick axe but the small drop ofwater.- Josemaria Escriva9. The object of the life is not to be close to the prototype of the majority, but toescape finding oneself in the cluster of the insane.- Marcus Aurelius, paraphrased10. Ende gut, alles gut. All is well that ends well.

   University of GroningenDivergences for prototype-based classification and causal structure discoveryMwebaze, ErnestIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2014Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mwebaze, E. (2014). Divergences for prototype-based classification and causal structure discovery: Theoryand application to natural datasets. [S.n.].CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 12-10-2022Stellingenbehorende bij het proefschriftDivergences for Prototype-based Classification andCausal Structure Discovery,Theory and Application to Natural DatasetsvanErnest Mwebaze1. Divergences can be used in prototype-based classification when dealing withdistributions and for potentially normalised data.2. Inferring underlying causal mechanisms in data is key to understanding theeffects of manipulations or interventions on the data.3. Under certain specific assumptions, it is possible to infer causality frompurely observational data.4. Successful application of machine learning algorithms to real problems shouldbe a significant way of validating the algorithms.5. Views related to this work may be divergent - this work is about divergencesafter all.6. There is an inevitable divergence between the world as it is and the worldas men perceive it.- J.William Fulbright7. Don’t forget that on earth, every big thing has had a small beginning. Whatis born big is monstrous and dies.8. The greatest enemy of the rock is not the pick axe but the small drop ofwater.- Josemaria Escriva9. The object of the life is not to be close to the prototype of the majority, but toescape finding oneself in the cluster of the insane.- Marcus Aurelius, paraphrased10. Ende gut, alles gut. All is well that ends well.

ARTS repository - University of Groningen

   University of GroningenDivergences for prototype-based classification and causal structure discoveryMwebaze, ErnestIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2014Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mwebaze, E. (2014). Divergences for prototype-based classification and causal structure discovery: Theoryand application to natural datasets. [Thesis fully internal (DIV), University of Groningen]. [S.n.].CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 05-12-2023Stellingenbehorende bij het proefschriftDivergences for Prototype-based Classification andCausal Structure Discovery,Theory and Application to Natural DatasetsvanErnest Mwebaze1. Divergences can be used in prototype-based classification when dealing withdistributions and for potentially normalised data.2. Inferring underlying causal mechanisms in data is key to understanding theeffects of manipulations or interventions on the data.3. Under certain specific assumptions, it is possible to infer causality frompurely observational data.4. Successful application of machine learning algorithms to real problems shouldbe a significant way of validating the algorithms.5. Views related to this work may be divergent - this work is about divergencesafter all.6. There is an inevitable divergence between the world as it is and the worldas men perceive it.- J.William Fulbright7. Don’t forget that on earth, every big thing has had a small beginning. Whatis born big is monstrous and dies.8. The greatest enemy of the rock is not the pick axe but the small drop ofwater.- Josemaria Escriva9. The object of the life is not to be close to the prototype of the majority, but toescape finding oneself in the cluster of the insane.- Marcus Aurelius, paraphrased10. Ende gut, alles gut. All is well that ends well.

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Divergences for prototype-based classification and causal structure discovery: Theory and application to natural datasets

University of Groningen Research Database

University of Groningen

   University of GroningenDivergences for prototype-based classification and causal structure discoveryMwebaze, ErnestIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2014Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mwebaze, E. (2014). Divergences for prototype-based classification and causal structure discovery: Theoryand application to natural datasets. [Thesis fully internal (DIV), University of Groningen]. [S.n.].CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 08-09-2023Stellingenbehorende bij het proefschriftDivergences for Prototype-based Classification andCausal Structure Discovery,Theory and Application to Natural DatasetsvanErnest Mwebaze1. Divergences can be used in prototype-based classification when dealing withdistributions and for potentially normalised data.2. Inferring underlying causal mechanisms in data is key to understanding theeffects of manipulations or interventions on the data.3. Under certain specific assumptions, it is possible to infer causality frompurely observational data.4. Successful application of machine learning algorithms to real problems shouldbe a significant way of validating the algorithms.5. Views related to this work may be divergent - this work is about divergencesafter all.6. There is an inevitable divergence between the world as it is and the worldas men perceive it.- J.William Fulbright7. Don’t forget that on earth, every big thing has had a small beginning. Whatis born big is monstrous and dies.8. The greatest enemy of the rock is not the pick axe but the small drop ofwater.- Josemaria Escriva9. The object of the life is not to be close to the prototype of the majority, but toescape finding oneself in the cluster of the insane.- Marcus Aurelius, paraphrased10. Ende gut, alles gut. All is well that ends well.

Divergences for prototype-based classification and causal structure discovery:Theory and application to natural datasets

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Dissertations of the University of Groningen

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University of Groningen

ARTS repository - University of Groningen

Proceedings - University of Groningen