Recent research has studied the role of sparsity in high dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models from sparse...
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
In corrective maintenance, selective regression testing includes test selection from previously run test suite and test coverage identification. We propose three reductionbased re...
We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for re...
Huber's M-estimation technique is applied to a block-angular regression problem, which may arise from some applications. A recursive, modified Newton approach to computing th...
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the depe...