55 citations · 117 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
A Hybrid Latent Variable Neural Network Model for Item Recommendation
Michael R. Smith, Tony Martinez, Michael Gashler
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filte…
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting
Michael R. Smith, Tony Martinez
Not all instances in a data set are equally beneficial for inferring a model of the data. Some instances (such as outliers) are detrimental to inferring a model of the data. Severa…
An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks
Michael R. Smith, Tony Martinez
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling o…