Add to ORKGo Consider an ensemble of models and stick with the best Brute Force Approach o Validate the ensemble on a common problem set o Select the champion model Trade-off o Benefit: Identify best model(s) o Price to be paid: Computational cost RAs trade off model optimality vs. computational effort by automatically allocating computational resource
The methodology based on computing budget allocation is an effective tool in solving the problem of ...
International audienceModern optimization strategies such as evolutionary algorithms, ant colony alg...
Automated algorithm configuration has been proven to be an effective approach for achieving improved...
Racing algorithms are often used for offline model selection, where models are compared in terms of ...
Racing algorithms are often used for offline model selection, where models are compared in terms of ...
Given a set of models and some training data, we would like to find the model which best describes t...
Model Selection (MS) is an important aspect of machine learning, as necessitated by the No Free Lunc...
Model selection is a core aspect in machine learning and is, occasionally, multi-objective in nature...
This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-ob...
This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-ob...
The 2016 IEEE World Congress on Computational Intelligence (IEEE WCCI 2016) host three conferences: ...
This thesis presents methods for minimizing the computational effort of problem solving. Rather than...
Abstract Algorithms for solving hard optimization problems typically have several parameters that ne...
In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare alg...
World Endurance Championship (WEC) racing events are characterised by a relevant performance gap amo...
The methodology based on computing budget allocation is an effective tool in solving the problem of ...
International audienceModern optimization strategies such as evolutionary algorithms, ant colony alg...
Automated algorithm configuration has been proven to be an effective approach for achieving improved...
Racing algorithms are often used for offline model selection, where models are compared in terms of ...
Racing algorithms are often used for offline model selection, where models are compared in terms of ...
Given a set of models and some training data, we would like to find the model which best describes t...
Model Selection (MS) is an important aspect of machine learning, as necessitated by the No Free Lunc...
Model selection is a core aspect in machine learning and is, occasionally, multi-objective in nature...
This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-ob...
This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-ob...
The 2016 IEEE World Congress on Computational Intelligence (IEEE WCCI 2016) host three conferences: ...
This thesis presents methods for minimizing the computational effort of problem solving. Rather than...
Abstract Algorithms for solving hard optimization problems typically have several parameters that ne...
In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare alg...
World Endurance Championship (WEC) racing events are characterised by a relevant performance gap amo...
The methodology based on computing budget allocation is an effective tool in solving the problem of ...
International audienceModern optimization strategies such as evolutionary algorithms, ant colony alg...
Automated algorithm configuration has been proven to be an effective approach for achieving improved...