55 citations · 117 across the 14 of their papers we have counts for
16 papers
A Hierarchical Multi-Output Nearest Neighbor Model for Multi-Output Dependence Learning
Richard G. Morris, Tony Martinez, Michael R. Smith
Multi-Output Dependence (MOD) learning is a generalization of standard classification problems that allows for multiple outputs that are dependent on each other. A primary issue th…
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…
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…
Reducing the Effects of Detrimental Instances
Michael R. Smith, Tony Martinez
Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially,…
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…