An adversarial environment classifier training system includes feature extraction circuitry to identify a number of features associated with each sample included in an initial data set that includes a plurality of samples. The system further includes sample allocation circuitry to allocate at least a portion of the samples included in the initial data set to at least a training data set; machine-learning circuitry communicably coupled to the sample allocation circuitry, the machine-learning circuitry to: identify at least one set of compromiseable features for at least a portion of the initial data set; define a classifier loss function [l(xi, yi, w)] that includes: a feature vector (xi) for each sample included in the initial data set; a label (yi) for each sample included in the initial data set; and a weight vector (w) associated with the classifier; and determine the minmax of the classifier loss function (minwmaxi l(xi, yi, w)).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An adversarial environment classifier training system, comprising: feature extraction circuitry to identify a number of features associated with each sample included in an initial data set that includes a plurality of samples; sample allocation circuitry to allocate at least a portion of the samples included in the initial data set to at least a training data set; machine-learning circuitry communicably coupled to the sample allocation circuitry, the machine-learning circuitry to: identify at least one set of compromiseable features for at least a portion of the samples included in the training data set; define a classifier loss function [l(xi, yi, w)] that includes: a feature vector (xi) for each sample included in the training data set; a label (yi) for each sample included in the training data set; and a weight vector (w) associated with the classifier; and determine the minmax of the classifier loss function (minwmaxi l(xi, yi, w)). 2. The system of claim 1 wherein the sample allocation circuitry further comprises circuitry to allocate at least a portion of the samples included in the initial data set to at least one of: a training data set; a testing data set; or a cross-validation data set. 3. The system of claim 1, the machine-learning circuitry to autonomously identify at least one set of compromiseable features for at least a portion of the samples included in the training data set. 4. The system of claim 1, the machine-learning circuitry to receive at least one input to manually identify at least one set of compromiseable features for at least a portion of the samples included in the training data set. 5. The system of claim 1, the machine-learning circuitry to: define a loss function that includes a first logical value for the label associated with a sample if the respective sample represents a non-malicious sample; and define a loss function that includes a second logical value for the label associated with a sample if the respective sample represents a malicious sample. 6. The system of claim 1, the machine-learning circuitry to further: identify a set consisting of a fixed number of compromiseable features for at least a portion of the samples included in the training data set. 7. The system of claim 1, the machine-learning circuitry to further: identify a plurality of sets of compromiseable features for at least a portion of the samples included in the training data set, each of the plurality of sets including a different number of compromiseable features for at least a portion of the samples included in the training data set. 8. A adversarial environment classifier training method, comprising: identifying a number of features associated with each sample included in an initial data set that includes a plurality of samples; allocating at least a portion of the samples included in the initial data set into at least a training data set; identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set; defining a classifier loss function [l(xi, yi, w)] that includes: a feature vector (xi) for each sample included in the training data set; a label (yi) for each sample included in the training data set; and a weight vector (w) associated with the classifier; and determining the minmax of the classifier loss function (minwmaxi l(xi, yi, w)). 9. The method of claim 8 wherein identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set comprises: autonomously identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set. 10. The method of claim 8 wherein identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set comprises: manually identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set. 11. The method of claim 8 wherein defining a classifier loss function [l(xi, yi, w)] that includes a label (yi) for each sample included in the training data set comprises: defining a loss function that includes a first logical value for the label associated with an sample if the respective sample represents a non-malicious sample; and defining a loss function that includes a second logical value for the label associated with an sample if the respective sample represents a malicious sample. 12. The method of claim 8 wherein identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set comprises: identifying a set consisting of a fixed number of compromiseable features for at least a portion of the samples included in the training data set. 13. The method of claim 8 wherein identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set comprises: identifying a plurality of sets of compromiseable features for at least a portion of the samples included in the training data set, each of the plurality of sets including a different number of compromiseable features for at least a portion of the samples included in the training data set. 14. A storage device that includes machine-readable instructions that, when executed, physically transform a configurable circuit to an adversarial machine-learning training circuit, the adversarial environment classifier training circuit to: identify a number of features associated with each sample included in a training data set that includes a plurality of samples; allocate at least a portion of the samples included in the initial data set to at least a training data set; identify at least one set of compromiseable features for at least a portion of the samples included in the training data set; define a classifier loss function [l(xi, yi, w)] that includes: a feature vector (xi) for each sample included in the training data set; a label (yi) for each sample included in the training data set; and a weight vector (w) associated with the classifier; and determine the minmax of the classifier loss function (minwmaxi l(xi, yi, w)). 15. The storage device of claim 14 wherein the machine-readable instructions that cause the adversarial environment classifier training circuit to identify at least one set of compromiseable features for at least a portion of the samples included in the training data set, cause the adversarial environment classifier training circuit to: autonomously identify at least one set of compromiseable features for at least a portion of the samples included in the training data set. 16. The storage device of claim 14 wherein the machine-readable instructions that cause the adversarial environment classifier training circuit to identify at least one set of compromiseable features for at least a portion of the samples included in the training data set, cause the adversarial environment classifier training circuit to: receive an input that includes data that manually identifies at least one set of compromiseable features for at least a portion of the samples included in the training data set. 17. The storage device of claim 14 wherein the machine-readable instructions that cause the adversarial environment classifier training circuit to define a classifier loss function [l(xi, yi, w)] that includes a label (yi) for each sample included in the training data set, further cause the adversarial environment classifier training circuit to: define a loss function that includes a first logical value for the label associated with an sample if the respective sample represents a non-malicious sample; and define a loss function that includes a second logical value for the label associated with an sample if the respective sample represents a malicious sample. 18. The storage device of claim 14 wherein the machine-readable instructions that cause the adversarial environment classifier training circuit to identify at least one set of compromiseable features for at least a portion of the samples included in the training data set, further cause the adversarial environment classifier training circuit to: identify a set consisting of a fixed number of compromiseable features for at least a portion of the samples included in the training data set. 19. The storage device of claim 14 wherein the machine-readable instructions that cause the adversarial environment classifier training circuit to identify at least one set of compromiseable features for at least a portion of the samples included in the training data set, further cause the adversarial environment classifier training circuit to: identify a plurality of sets of compromiseable features for at least a portion of the samples included in the training data set, each of the plurality of sets including a different number of compromiseable features for at least a portion of the samples included in the training data set. 20. A classifier training system, comprising: a means for identifying a number of features associated with each sample included in an initial data set that includes a plurality of samples; a means for allocating at least a portion of the samples included in the initial data set into at least a training data set; a means for identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set; a means for defining a classifier loss function [l(xi, yi, w)] that includes: a feature vector (xi) for each sample included in the training data set; a label (yi) for each sample included in the training data set; and a weight vector (w) associated with the classifier; and a means for determining the minmax of the classifier loss function (minwmaxi l(xi, yi, w)). 21. The system of claim 20 wherein the means for identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set comprises: a means for autonomously identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set. 22. The system of claim 20 wherein the means for identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set comprises: a means for manually identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set. 23. The system of claim 20 wherein the means for defining a classifier loss function [l(xi, yi, w)] that includes a label (yi) for each sample included in the training data set comprises: a means for defining a loss function that includes a first logical value for the label associated with an sample if the respective sample represents a non-malicious sample; and a means for defining a loss function that includes a second logical value for the label associated with an sample if the respective sample represents a malicious sample. 24. The system of claim 20 wherein the means for identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set comprises: a means for identifying a set consisting of a fixed number of compromiseable features for at least a portion of the samples included in the training data set. 25. The system of claim 20 wherein identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set comprises: a means for identifying a plurality of sets of compromiseable features for at least a portion of the samples included in the training data set, each of the plurality of sets including a different number of compromiseable features for at least a portion of the samples included in the training data set.