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Intelligent Database Systems Lab Presenter: Wu, Jhen-Wei Authors: Fabian Bürger, Josef Pauli 2015. ICPRAM. Representation Optimization with Feature Selection and Manifold Learning in a Holistic Classification Framework
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Intelligent Database Systems Lab Outlines Motivation Objectives Methods Experiments Conclusions Comments 2
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Intelligent Database Systems Lab Motivation The performance of a manifold learner heavily depends on the dataset. A recent research regarding the dimension reduction is turned out to be relatively slow and ineffective for high-dimensional datasets. 3
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Intelligent Database Systems Lab Objectives Develop a framework that incorporates multiple manifold learning algorithms in a holistic classification pipeline. 4
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Intelligent Database Systems Lab 5 Methods
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Intelligent Database Systems Lab 6 Methods
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Intelligent Database Systems Lab 7 Methods
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Intelligent Database Systems Lab 8 Methods
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Intelligent Database Systems Lab 9 Methods
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Intelligent Database Systems Lab 10 Methods
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Intelligent Database Systems Lab 11 Methods
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Intelligent Database Systems Lab 12 Methods ES: (μ/ρ+λ)
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Intelligent Database Systems Lab 13 Experiments
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Intelligent Database Systems Lab 14 Experiments
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Intelligent Database Systems Lab 15 Experiments
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Intelligent Database Systems Lab 16 Experiments
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Intelligent Database Systems Lab Conclusions The concept of multi-pipeline classifiers offers a better performance, but comes along with higher computational costs. 17
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Intelligent Database Systems Lab Comments Contribution –Present a novel automatic optimization framework that incorporates multiple manifold learning algorithms in a holistic classification pipeline. Applications –Evolutionary Optimization, Classification 18
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