Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imba...
Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identicaldistribution assumpt...
We consider the problem of bounded model checking of systems expressed in a decidable fragment of first-order logic. While model checking is not guaranteed to terminate for an ar...
Randal E. Bryant, Shuvendu K. Lahiri, Sanjit A. Se...
The problem of learning linear discriminant concepts can be solved by various mistake-driven update procedures, including the Winnow family of algorithms and the well-known Percep...