The convergence rate of stochastic gradient search is analyzed in this paper. Using arguments based on differential geometry and Lojasiewicz inequalities, tight bounds on the convergence rate of general stochastic gradient algorithms are derived. As opposed to the existing results, the results presented in this paper allow the objective function to have multiple, non-isolated minima, … Read more
We present a method to tune software parameters using ideas from software testing and machine learning. The method is based on the key observation that for many classes of instances, the software shows improved performance if a few critical parameters have “good” values, although which parameters are critical depends on the class of instances. Our … Read more
This report introduces a new machine learning paradigm called enclosing machine learning for data mining. This novel method utilizes the virtues of human being’s cognition process and tries to imitate the two basic principles of cognition process from a macroscopical view, which are cognizing things of the same kind, recognizing things of a new kind … Read more