A semi-Bayesian method for nonparametric density estimation
If x([1],) ..., x([n]) are the ordered outcomes of an independent random sample from a distribution on the interval [x([0]), x([n+1])] = [a, b] with distribution function F and density function f then 'intrinsic' arguments (Section 2) lead to a specific Bernstein polynomial as 'the' estimate of F-1(p). Numerical inversion and differentiation provides 'the' estimate of f = F' which we study in detail. A comparison is made, extensions are indicated and literature is discussed. (C) 1999 Elsevier Science B.V. All rights reserved.
This paper researches on problems of improving the stability of feature selection algorithm. A bagging-based selective results ensemble method is proposed. First use a feature selection algorithm and different training subsets to select several feature subsets. Then compute weights of each selected feature subset by mutual information and classifying accuracy. At last use a bagging-based method to assemble the selective subsets. Experiments in intrusion detection data of KDD cup'99 show that this algorithm could obtain better results.