students:phd_mlws
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students:phd_mlws [2017/05/28 17:37] – [Bibliographie] blay | students:phd_mlws [2017/05/28 17:39] – [Objectives] blay | ||
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The thesis must address the following challenges: Relevance and quality of predictions and Scalability to manage the huge mass of ML workflows. | The thesis must address the following challenges: Relevance and quality of predictions and Scalability to manage the huge mass of ML workflows. | ||
To meet these challenges, attention should be paid to the following aspects: | To meet these challenges, attention should be paid to the following aspects: | ||
- | * //Handling Variabilities: | + | * //Handling Variabilities: |
*// Architecture of the portfolio : // automatically manage (1) experiment running, (2) collecting of experiment results, (3) analyzis of results, (4) evolution of algorithm base. It must support the management of execution errors, incremental analyzes, identifying context of experiments. | *// Architecture of the portfolio : // automatically manage (1) experiment running, (2) collecting of experiment results, (3) analyzis of results, (4) evolution of algorithm base. It must support the management of execution errors, incremental analyzes, identifying context of experiments. | ||
* //Handling Scalability of the Portfolio: //Selecting discriminating data sets; Detecting “deprecated” algorithms and WF from experiments and literature revues; Dealing with information from scientific literature without deteriorating portfolio computed knowledge. | * //Handling Scalability of the Portfolio: //Selecting discriminating data sets; Detecting “deprecated” algorithms and WF from experiments and literature revues; Dealing with information from scientific literature without deteriorating portfolio computed knowledge. |
students/phd_mlws.txt · Last modified: 2017/05/28 18:03 by blay