Probabilistic mixture models are used for a broad range of data analysis tasks such as clustering, classification, predictive modeling, etc. Due to their inherent probabilistic na...
In many scientific and engineering applications, detecting and understanding differences between two groups of examples can be reduced to a classical problem of training a classif...
There are several results available in the literature dealing with efficient construction of t-spanners for a given set S of n points in Rd. t-spanners are Euclidean graphs in whic...
We consider the existence of a linear weak learner for boosting algorithms. A weak learner for binary classification problems is required to achieve a weighted empirical error on t...
Cross validation allows models to be tested using the full training set by means of repeated resampling; thus, maximizing the total number of points used for testing and potential...