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Machine Learning Algorithms Vom Gradientenabstieg über Monte Carlo bis hin zu Genetischen Algorithmen Martin Szugat (Martin.Szugat@GMX.net) Hauptseminar: „The Machine Learning Approach“ Im WS 2003/04 bei Prof. Dr. Zimmer

Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Bayes‘sche Schlussfolgerung ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],log P(M|D) = log P(D|M) + log P(M) - log P(D)

Modellbildung ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Dynamische Programmierung ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Gradientenabstieg ,[object Object],[object Object],[object Object],[object Object],[object Object]

GA - Anwendung ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

GA - Beispiel ,[object Object],[object Object],[object Object],[object Object],2 / 2 n ... 0,125 0,25 0,5 1 2 w t 0,125 4 ... ... 0,5 2 0,25 3 2 / 2 n n 1 1 / 0 ∆ w t

EM/GEM-Algorithmus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

EM – Prinzip I ,[object Object],[object Object],[object Object],[object Object]

EM – Freie Energie ,[object Object],[object Object],[object Object]

EM – Prinzip II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

EM – Zusammenfassung ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Markov-Chain Monte-Carlo Methoden ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Markov-Ketten I ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Markov-Ketten II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Gibbs-Sampling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Metropolis Algorithmus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Metropolis - Verteilungen ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Metropolis - Energie ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Simulated Annealing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Boltzmann-Gibbs-Verteilung ,[object Object],[object Object]

SA - Vorgehensweise ,[object Object],[object Object],[object Object],[object Object],[object Object]

Annealing Schedule ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Genetische Algorithmen ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Proteinstrukturalignierung mittels Genetischem Algorithmus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Proteinstrukturalignierung ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

KENOBI - Methode ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Fitnessfunktion: Elastic Similarity Score I ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Envelope-Funktion *: Durchschnitt Abweichung

Fitnessfunktion: Elastic Similarity Score II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

1. Phase: Genetischer Algorithmus ,[object Object],[object Object],[object Object],[object Object],[object Object]

1. Phase: Anfängliche Population ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

1. Phase: Operatoren I ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

1. Phase: Operatoren II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

1. Phase: Fitness der Population ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

1. Phase: Abbruch des GA ,[object Object],[object Object],[object Object],[object Object],[object Object]

2. Phase: Erweiterung ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

KENOBI – Diskussion ,[object Object],[object Object],[object Object],[object Object],[object Object]

Zusammenfassung ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Datenauswahl ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Modellkomplexität ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Noch Fragen??? ,[object Object]

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