Add to ORKGInternational audienceMachine learning has proven to be a powerful tool in diverse fields, and is getting more and more widely used by non-experts. One of the foremost difficulties they encounter lies in the choice and calibration of the machine learning algorithm to use. Our objective is thus to provide assistance in the matter, using a meta-learning approach based on an evolutionary heuristic. We expand here previous work presenting the intended workflow of a modeling assistant by describing the characterization of learning instances we intend to use
In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springe...
In the last years, organizations and companies in general have found the true potential value of col...
While artificial learning agents have demonstrated impressive capabilities, these successes are typi...
International audienceMachine learning has proven to be a powerful tool in diverse fields, and is ge...
The many machine learning and data mining techniques produced over the last decades can prove invalu...
An evolutionary algorithm (EA) is a biologically inspired metaheuristic that uses mutation, crossove...
International audienceMeta-learning has been widely studied and implemented in many Automated Machin...
Humans have a remarkable ability to learn new concepts from only a few examples and quickly adapt to...
This book introduces numerous algorithmic hybridizations between both worlds that show how machine l...
The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independe...
The field of artificial intelligence has been throughout its history repeatedly inspired by human co...
The majority of the algorithms used to solve hard optimization problems today are population metaheu...
Meta-learning, or learning to learn, is an emerging field within artificial intelligence (AI) that e...
While Machine Learning (ML) techniques enjoyed growing popularity in recent years, the role of Evolu...
A number of methodological papers published during the last years testify that a need for a thorough...
In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springe...
In the last years, organizations and companies in general have found the true potential value of col...
While artificial learning agents have demonstrated impressive capabilities, these successes are typi...
International audienceMachine learning has proven to be a powerful tool in diverse fields, and is ge...
The many machine learning and data mining techniques produced over the last decades can prove invalu...
An evolutionary algorithm (EA) is a biologically inspired metaheuristic that uses mutation, crossove...
International audienceMeta-learning has been widely studied and implemented in many Automated Machin...
Humans have a remarkable ability to learn new concepts from only a few examples and quickly adapt to...
This book introduces numerous algorithmic hybridizations between both worlds that show how machine l...
The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independe...
The field of artificial intelligence has been throughout its history repeatedly inspired by human co...
The majority of the algorithms used to solve hard optimization problems today are population metaheu...
Meta-learning, or learning to learn, is an emerging field within artificial intelligence (AI) that e...
While Machine Learning (ML) techniques enjoyed growing popularity in recent years, the role of Evolu...
A number of methodological papers published during the last years testify that a need for a thorough...
In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springe...
In the last years, organizations and companies in general have found the true potential value of col...
While artificial learning agents have demonstrated impressive capabilities, these successes are typi...