Search Results for author: Michael R. Smith

Found 15 papers, 0 papers with code

Self-Updating Models with Error Remediation

no code implementations19 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.

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security

no code implementations4 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.

Dynamic Analysis of Executables to Detect and Characterize Malware

no code implementations10 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.

A Minimal Architecture for General Cognition

no code implementations31 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.

A Hierarchical Multi-Output Nearest Neighbor Model for Multi-Output Dependence Learning

no code implementations17 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.

General Classification

Recommending Learning Algorithms and Their Associated Hyperparameters

no code implementations7 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.


A Hybrid Latent Variable Neural Network Model for Item Recommendation

no code implementations9 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.

Reducing the Effects of Detrimental Instances

no code implementations9 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.

The Potential Benefits of Filtering Versus Hyper-Parameter Optimization

no code implementations13 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.

Becoming More Robust to Label Noise with Classifier Diversity

no code implementations7 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.

Missing Value Imputation With Unsupervised Backpropagation

no code implementations19 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.

General Classification Imputation

An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks

no code implementations13 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.

General Classification

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