Opposition-based learning as a new scheme for machine intelligence is introduced. Estimates and counter-estimates, weights and opposite weights, and actions versus counter-actions...
Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical m...
Blaine Nelson, Marco Barreno, Fuching Jack Chi, An...
We show that randomly generated monotone c log(n)-DNF formula can be learned exactly in probabilistic polynomial time. Our notion of randomly generated is with respect to a unifor...
Some machine learning applications are intended to learn properties of data sets where the correct answers are not already known to human users. It is challenging to test such ML ...
We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, w...