no code implementations • 10 Nov 2017 • Michael R. Smith, Joe B. Ingram, Christopher C. Lamb, Timothy J. Draelos, Justin E. Doak, James B. Aimone, Conrad D. James
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life.
no code implementations • 21 Mar 2017 • Michael R. Smith, Aaron J. Hill, Kristofor D. Carlson, Craig M. Vineyard, Jonathon Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James B. Aimone
Information in neural networks is represented as weighted connections, or synapses, between neurons.
no code implementations • 31 Jul 2015 • Michael S. Gashler, Zachariah Kindle, Michael R. Smith
From this perspective, MANIC offers an alternate approach to a long-standing objective of artificial intelligence.
no code implementations • 17 Oct 2014 • 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.
no code implementations • 9 Jun 2014 • Michael R. Smith, Tony Martinez
We examine RIDL on a set of 54 data sets and 5 learning algorithms and compare RIDL with other weighting and filtering approaches.
no code implementations • 7 Jul 2014 • Michael R. Smith, Logan Mitchell, Christophe Giraud-Carrier, Tony Martinez
The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters.
no code implementations • 9 Jun 2014 • 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.
no code implementations • 28 May 2014 • Michael R. Smith, Andrew White, Christophe Giraud-Carrier, Tony Martinez
The results from most machine learning experiments are used for a specific purpose and then discarded.
no code implementations • 13 Mar 2014 • Michael R. Smith, Tony Martinez, Christophe Giraud-Carrier
We find that, while both significantly improve the induced model, improving the quality of the training set has a greater potential effect than hyper-parameter optimization.
no code implementations • 7 Mar 2014 • Michael R. Smith, Tony Martinez
We compare NICD with several other noise handling techniques that do not consider classifier diversity on a set of 54 data sets and 5 learning algorithms.
no code implementations • 19 Dec 2013 • Michael S. Gashler, Michael R. Smith, Richard Morris, Tony Martinez
We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques.
no code implementations • 17 Dec 2013 • Michael R. Smith, Tony Martinez
Some instances (such as outliers) are detrimental to inferring a model of the data.
no code implementations • 13 Dec 2013 • Michael R. Smith, Tony Martinez
Additionally, we find that a majority voting ensemble is robust to noise as filtering with a voting ensemble does not increase the classification accuracy of the voting ensemble.
no code implementations • 4 May 2020 • Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, Armida J. Carbajal, Ramyaa Ramyaa, Evelyn Domschot, Christopher C. Lamb, Stephen J. Verzi, W. Philip Kegelmeyer
Specifically, current datasets and representations used by ML are not suitable for learning the behaviors of an executable and differ significantly from those used by the InfoSec community.
no code implementations • 19 May 2020 • Justin E. Doak, Michael R. Smith, Joey B. Ingram
We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation.