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Found 15 papers, 0 papers with code
Self-Updating Models with Error Remediation
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
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
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life.
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or synapses, between neurons.
A Minimal Architecture for General Cognition
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
Multi-Output Dependence (MOD) learning is a generalization of standard classification problems that allows for multiple outputs that are dependent on each other.
Recommending Learning Algorithms and Their Associated Hyperparameters
The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters.
Reducing the Effects of Detrimental Instances
We examine RIDL on a set of 54 data sets and 5 learning algorithms and compare RIDL with other weighting and filtering approaches.
A Hybrid Latent Variable Neural Network Model for Item Recommendation
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques.
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage
The results from most machine learning experiments are used for a specific purpose and then discarded.
The Potential Benefits of Filtering Versus Hyper-Parameter Optimization
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
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
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.
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting
Some instances (such as outliers) are detrimental to inferring a model of the data.
An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks
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.
