A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise.
Beilun WangJi GaoYanjun QiPublished in: CoRR (2016)
Keyphrases
- theoretical framework
- greater robustness
- training data
- theoretical foundation
- noisy data
- decision trees
- multi agent
- support vector
- machine learning algorithms
- missing data
- training set
- conceptual framework
- machine learning
- naive bayes
- supervised classification
- collaborative knowledge building
- feature selection
- feature set