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You searched for subject:(Relational Kalman filter). Showing records 1 – 2 of 2 total matches.

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University of Illinois – Urbana-Champaign

1. Choi, Jaesik. Lifted Inference for Relational Hybrid Models.

Degree: PhD, 0112, 2012, University of Illinois – Urbana-Champaign

Probabilistic Graphical Models (PGMs) promise to play a prominent role in many complex real-world systems. Probabilistic Relational Graphical Models (PRGMs) scale the representation and learning of PGMs. Answering questions using PRGMs enables many current and future applications, such as medical informatics, environmental engineering, financial forecasting and robot localizations. Scaling inference algorithms for large models is a key challenge for scaling up current applications and enabling future ones. This thesis presents new insights into large-scale probabilistic graphical models. It provides fresh ideas for maintaining a compact structure when answering questions or inferences about large, continuous models. The insights result in a key contribution, the Lifted Relational Kalman filter (LRKF), an efficient estimation algorithm for large-scale linear dynamic systems. It shows that the new relational Kalman filter enables scaling the exact vanilla Kalman filter from 1,000 to 1,000,000,000 variables. Another key contribution of this thesis is that it proves that typically used probabilistic first-order languages, including Markov Logic Networks (MLNs) and First-Order Probabilistic Models (FOPMs), can be reduced to compact probabilistic graphical representations under reasonable conditions. Specifically, this thesis shows that aggregate operators and the existential quantification in the languages are accurately approximated by linear constraints in the Gaussian distribution. In general, probabilistic first-order languages are transformed into nonparametric variational models where lifted inference algorithms can efficiently solve inference problems. Advisors/Committee Members: Amir, Eyal (advisor), Amir, Eyal (Committee Chair), Roth, Dan (committee member), LaValle, Steven M. (committee member), Poole, David (committee member).

Subjects/Keywords: Probabilistic Graphical Models; Relational Hybrid Models; Lifted Inference; First-Order Probabilistic Models; Probabilistic Logic; Kalman filter; Relational Kalman filter; Variational Learning, Markov Logic Networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Choi, J. (2012). Lifted Inference for Relational Hybrid Models. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/32004

Chicago Manual of Style (16th Edition):

Choi, Jaesik. “Lifted Inference for Relational Hybrid Models.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 06, 2020. http://hdl.handle.net/2142/32004.

MLA Handbook (7th Edition):

Choi, Jaesik. “Lifted Inference for Relational Hybrid Models.” 2012. Web. 06 Apr 2020.

Vancouver:

Choi J. Lifted Inference for Relational Hybrid Models. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2020 Apr 06]. Available from: http://hdl.handle.net/2142/32004.

Council of Science Editors:

Choi J. Lifted Inference for Relational Hybrid Models. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/32004


Brno University of Technology

2. Chytka, Karel. Získávání znalostí z objektově relačních databází .

Degree: 2008, Brno University of Technology

Cílem této diplomové práce je seznámit se s problematikou získávání znalostí a klasifikací objektově relačních dat. Práce dále navazuje  a aplikaci získávání znalostí v objektově relačních databázích. Práce shrnuje problémy spojené s dolování v casoprostorových datech. Konkrétně jsou zde probírány vlastnosti jádrového algoritmu SVM pro data mining. Druhá cást práce se zabývá implementací klasifikacní metody pro získávání znalostí z trajektorií pohybujících se objektů z projektu Caretaker. Dále je součástí práce implementace aplikace pro předzpracování časoprostorových dat, jejich organizaci v databázi a prezentaci časoprostorových dat.; The goal of this master's thesis is to acquaint with a problem of a knowledge discovery and objectrelational data classification. It summarizes problems which are connected with mining spatiotemporal data. There is described data mining kernel algorithm SVM. The second part solves classification method implementation. This method solves data mining in a Caretaker trajectory database. This thesis contains application's implementation for spatio-temporal data preprocessing, their organization in database and presentation too. Advisors/Committee Members: Chmelař, Petr (advisor).

Subjects/Keywords: Získávání znalostí; Objektově-relační data; Časoprostorová data; Trajektorie; Support Vector Machines (SVM); Strojové učení;   jádrová funkce; Kalmanův filtr.; Knowledge discovery; Object-relational data; Spatio-temporal data; Trajectories; Support Vector Machines (SVM); Kernel function; Machine learning; Kalman filter.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chytka, K. (2008). Získávání znalostí z objektově relačních databází . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53113

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chytka, Karel. “Získávání znalostí z objektově relačních databází .” 2008. Thesis, Brno University of Technology. Accessed April 06, 2020. http://hdl.handle.net/11012/53113.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chytka, Karel. “Získávání znalostí z objektově relačních databází .” 2008. Web. 06 Apr 2020.

Vancouver:

Chytka K. Získávání znalostí z objektově relačních databází . [Internet] [Thesis]. Brno University of Technology; 2008. [cited 2020 Apr 06]. Available from: http://hdl.handle.net/11012/53113.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chytka K. Získávání znalostí z objektově relačních databází . [Thesis]. Brno University of Technology; 2008. Available from: http://hdl.handle.net/11012/53113

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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