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students:phd_2019
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students:phd_2019 [2019/05/10 20:31] blay [References] |
students:phd_2019 [2019/05/10 20:32] blay [Objectives] |
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| ===== Objectives ===== | ===== Objectives ===== | ||
| The construction of a portfolio requires covering a space of experiments broad enough to "cover" all the problems that may be submitted to it. | The construction of a portfolio requires covering a space of experiments broad enough to "cover" all the problems that may be submitted to it. | ||
| - | However, (1) the space of problems and solutions presents a very great ((The variability subjects are related to pretreatment, algorithms, datasets, evaluation criteria, experimental results. Each subject has several variants. For example, in OpenML, for each dataset downloaded, 61 dataset meta-features are calculated[18]. There are more than a hundred classification algorithms[5], etc.)) “diversity” [16] even within a single class of problem like classification [9]. | + | However, (1) the space of problems and solutions presents a very great ((The variability subjects are related to pretreatment, algorithms, datasets, evaluation criteria, experimental results. Each subject has several variants. For example, in OpenML, for each dataset downloaded, 61 dataset meta-features are calculated[17]. There are more than a hundred classification algorithms[5], etc.)) “diversity” [16] even within a single class of problem like classification [9]. |
| (2) The resources required for ML experiments are massive (time, memory, energy)((The number of theoretical experiments to study p pretreatments, n algorithms and d data sets is 2^p*n*d. For 10 preprocessing algorithms, 100 classification algorithms and 100 sets of data, considering that each experiment only lasts one minute, it would take more than 7000 days of execution time.)). | (2) The resources required for ML experiments are massive (time, memory, energy)((The number of theoretical experiments to study p pretreatments, n algorithms and d data sets is 2^p*n*d. For 10 preprocessing algorithms, 100 classification algorithms and 100 sets of data, considering that each experiment only lasts one minute, it would take more than 7000 days of execution time.)). | ||
| (3) As the ML domain is particularly productive, the portfolio must be able to evolve to integrate new algorithms. | (3) As the ML domain is particularly productive, the portfolio must be able to evolve to integrate new algorithms. | ||
students/phd_2019.txt · Last modified: 2019/05/10 20:40 by blay
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