9 citations · 33 across the 10 of their papers we have counts for
12 papers
Self-Updating Models with Error Remediation
Justin E. Doak, Michael R. Smith, Joey B. Ingram
Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the mode…
A Minimal Architecture for General Cognition
Michael S. Gashler, Zachariah Kindle, Michael R. Smith
A minimalistic cognitive architecture called MANIC is presented. The MANIC architecture requires only three function approximating models, and one state machine. Even with so few m…
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…
Becoming More Robust to Label Noise with Classifier Diversity
Michael R. Smith, Tony Martinez
It is widely known in the machine learning community that class noise can be (and often is) detrimental to inducing a model of the data. Many current approaches use a single, often…
Missing Value Imputation With Unsupervised Backpropagation
Michael S. Gashler, Michael R. Smith, Richard Morris +1
Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classific…
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…