Search for dissertations about: "ensemble"
Showing result 1 - 5 of 271 swedish dissertations containing the word ensemble.
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1. Free Ensemble Improvisation
Abstract : The aim of this doctoral project has been to study so-called non-idiomatic improvisation in ensembles consisting of two or three musicians who play together without any restrictions regarding style or genre and without having predetermined what is to be played or how they should play. The background to this thesis has been the author’s own free improvising, which he has pursued since 1974, and the questions that have arisen whilst music-making. READ MORE
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2. Utilizing Diversity and Performance Measures for Ensemble Creation
Abstract : An ensemble is a composite model, aggregating multiple base models into one predictive model. An ensemble prediction, consequently, is a function of all included base models. Both theory and a wealth of empirical studies have established that ensembles are generally more accurate than single predictive models. READ MORE
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3. Optimal weather routing using ensemble weather forecasts
Abstract : Ships small and large all battle the elements when crossing the worlds oceans. As such, ships are designed to operate in situations with high speed winds and heavy waves. There are however limits to what any ship can handle safely and it is thus important to avoid the worst weather systems as much as possible. READ MORE
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4. What is music education? : discursive construction and legitimisation of theory and practice in a Swedish upper secondary school
Abstract : The overall purpose of this thesis is to describe and discuss the discursive constructions and legitimisations of Music and Music theory in Swedish upper secondary school context. Thereby, this thesis is part of the construction and debate concerning theory vs practice in Music education. READ MORE
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5. Nonconformity Measures and Ensemble Strategies : An Analysis of Conformal Predictor Efficiency and Validity
Abstract : Conformal predictors are a family of predictive models that associate with each of their predictions a measure of confidence, enabling them to provide quantitative information about their own trustworthiness. In risk-laden machine learning applications, where bad predictions may lead to economic loss, personal injury, or worse, such inherent quality control appears highly beneficial, if not required. READ MORE