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| team:daniel_nyga [2017/10/16 11:46] – [Teaching] nyga | team:daniel_nyga [2018/07/03 10:52] – [Teaching] nyga |
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| showcase that PRAC is capable of closing the loop from | showcase that PRAC is capable of closing the loop from |
| natural-language instructions to their execution by a robot. | natural-language instructions to their execution by a robot. |
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| | ====Master's Thesis==== |
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| | [[https://ai.uni-bremen.de/_media/team/ma_nyga_small.pdf|{{:team:ma-nyga-cover.png?180 |}}]]//Abstract//-- This thesis investigates boosting algorithms for classifier learning in the presence of imbalanced classes and uneven misclassification costs. In particular, we address the well-known AdaBoost procedure and its extensions for coping with class imbalance, which typically has a negative impact on the classification accuracy regarding the minority class. We give an extensive survey of existing boosting methods for classification and enhancements for tackling the class imbalance problem, including cost-sensitive variants. Regularized boosting methods, which are favourable when dealing with noise and overlapping class distributions, are considered too. We theoretically analyze several strategies for introducing costs and their applicability in the case of imbalance. For one variant (AdaC1) we show that it is instable under certain conditions. We identify drawbacks of an often-cited cost-sensitive boosting algorithm (AdaCost), both theoretically and empirically. We also expose that an algorithm for tackling imbalance without using explicit costs (RareBoost) is a special case of the RealBoost algorithm, a probabilistic variant of AdaBoost. We approve our findings by empirical evaluation on several real-world data sets and academic benchmarks. |
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| ====Projects==== | ====Projects==== |
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| ====Teaching==== | ====Teaching==== |
| | * Foundations of Artificial Intelligence ([[https://ai.uni-bremen.de/teaching/kiss2018|SS2018]]) (Lecture) |
| * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws17|WS2017/18]]) (Lecturer) | * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws17|WS2017/18]]) (Lecturer) |
| * Master Seminar: Data Mining and Data Analytics ([[http://ai.uni-bremen.de/teaching/datamining_ss17|SS2017]]) | * Master Seminar: Data Mining and Data Analytics ([[http://ai.uni-bremen.de/teaching/datamining_ss17|SS2017]]) |
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