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students:phd_2019
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students:phd_2019 [2019/05/10 20:38] blay [Meta-learning in a Portfolio of Machine Learning Workflows] |
students:phd_2019 [2019/05/10 20:38] blay [Context] |
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| - | Advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. To determine the right ML workflow for a given problem, numerous parameters have to be taken in account: the kind of data, expected predictions (error, accuracy, time, memory space), the choice of the algorithms and their judicious composition [14,11]. | + | Advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. To determine the right ML workflow for a given problem, numerous parameters have to be taken in account: the kind of data, expected predictions (error, accuracy, time, memory space), the choice of the algorithms and their judicious composition [14,11]. \\ |
| - | To help with this task, Microsoft Azure Machine Learning, Amazon AWS, and RapidMiner Auto Model[12] provide ML component assembly tools. However, faced with the complexity of choosing the "right" assembly, meta-learning offers an attractive solution, learning from the problems of the past. The algorithm selection problem is one of its applications [5]: given a dataset, identify which learning algorithm (and which hyperparameter setting) performs best on it. | + | To help with this task, Microsoft Azure Machine Learning, Amazon AWS, and RapidMiner Auto Model[12] provide ML component assembly tools. However, faced with the complexity of choosing the "right" assembly, meta-learning offers an attractive solution, learning from the problems of the past. The algorithm selection problem is one of its applications [5]: given a dataset, identify which learning algorithm (and which hyperparameter setting) performs best on it.\\ |
| Algorithm Portfolio generalizes the problem and automates the construction of selection models [8]. The immediate goal is the same: to predict the results of the algorithms on a given problem without executing them. Even if, in the portfolio, some selection models are built by meta-learning [1], the purpose is different. The portfolio is based on the systematic acquisition of knowledge about the algorithms it contains. The research then focuses on the quality and the return of knowledge, the acquisition process itself and the construction of selection models over time. | Algorithm Portfolio generalizes the problem and automates the construction of selection models [8]. The immediate goal is the same: to predict the results of the algorithms on a given problem without executing them. Even if, in the portfolio, some selection models are built by meta-learning [1], the purpose is different. The portfolio is based on the systematic acquisition of knowledge about the algorithms it contains. The research then focuses on the quality and the return of knowledge, the acquisition process itself and the construction of selection models over time. | ||
students/phd_2019.txt · Last modified: 2019/05/10 20:40 by blay
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