9 citations · 26 across the 5 of their papers we have counts for
5 papers
Preprocessor Selection for Machine Learning Pipelines
Brandon Schoenfeld, Christophe Giraud-Carrier, Mason Poggemann +2
Much of the work in metalearning has focused on classifier selection, combined more recently with hyperparameter optimization, with little concern for data preprocessing. Yet, it i…
Recommending Learning Algorithms and Their Associated Hyperparameters
Michael R. Smith, Logan Mitchell, Christophe Giraud-Carrier +1
The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate lea…
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage
Michael R. Smith, Andrew White, Christophe Giraud-Carrier +1
The results from most machine learning experiments are used for a specific purpose and then discarded. This results in a significant loss of information and requires rerunning expe…
The Potential Benefits of Filtering Versus Hyper-Parameter Optimization
Michael R. Smith, Tony Martinez, Christophe Giraud-Carrier
The quality of an induced model by a learning algorithm is dependent on the quality of the training data and the hyper-parameters supplied to the learning algorithm. Prior work has…
An Integrated Framework for Learning and Reasoning
C. G. Giraud-Carrier, T. R. Martinez
Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine lear…