In this paper, we study the problem of employ ensemble learning for computer forensic. We propose a Lazy Local Learning based bagging （L3B） approach, where base learners are trained from a small instance subset surr...In this paper, we study the problem of employ ensemble learning for computer forensic. We propose a Lazy Local Learning based bagging （L3B） approach, where base learners are trained from a small instance subset surrounding each test instance. More specifically, given a test instance x, L3B first discovers x＇s k nearest neighbours, and then applies progressive sampling to the selected neighbours to train a set of base classifiers, by using a given very weak （VW） learner. At the last stage, x is labeled as the most frequently voted class of all base classifiers. Finally, we apply the proposed L3B to computer forensic.展开更多
The authors obtain a complex Hessian comparison for almost Hermitian manifolds, which generalizes the Laplacian comparison for almost Hermitian manifolds by Tossati, and a sharp spectrum lower bound for compact quasi ...The authors obtain a complex Hessian comparison for almost Hermitian manifolds, which generalizes the Laplacian comparison for almost Hermitian manifolds by Tossati, and a sharp spectrum lower bound for compact quasi Kahler manifolds and a sharp complex Hessian comparison on nearly Kahler manifolds that generalize previous results of Aubin, Li Wang and Tam-Yu.展开更多
基金the National High Technology Research and Development Program(863) of China(No.2007AA01Z456)the National Natural Science Foundation of China(No.60703030)
文摘In this paper, we study the problem of employ ensemble learning for computer forensic. We propose a Lazy Local Learning based bagging （L3B） approach, where base learners are trained from a small instance subset surrounding each test instance. More specifically, given a test instance x, L3B first discovers x＇s k nearest neighbours, and then applies progressive sampling to the selected neighbours to train a set of base classifiers, by using a given very weak （VW） learner. At the last stage, x is labeled as the most frequently voted class of all base classifiers. Finally, we apply the proposed L3B to computer forensic.
基金supported by the National Natural Science Foundation of China(No.11571215)the Natural Science Foundation of Guangdong Province(No.S2012010010038)a Supporting Project from the Department of Education of Guangdong Province(No.Yq2013073)
文摘The authors obtain a complex Hessian comparison for almost Hermitian manifolds, which generalizes the Laplacian comparison for almost Hermitian manifolds by Tossati, and a sharp spectrum lower bound for compact quasi Kahler manifolds and a sharp complex Hessian comparison on nearly Kahler manifolds that generalize previous results of Aubin, Li Wang and Tam-Yu.
