MOTIVATION Predicting peptides that bind to both Major Histocompatibility Complex (MHC) molecules and T cell receptors provides crucial information for vaccine development. An agretope is that portion of a peptide that interacts with an MHC molecule. The identification and prediction of agretopes is the first step towards vaccine design. RESULTS An iterative stepwise discriminant analysis met...

For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system depends critically on the similarity measure used. Based on a recently proposed semisupervised metric learning method called locally linear metric adaptation (LLMA), we propose in this paper a stepwise LLMA algorithm for boosting the retrieval performance of CBIR syste...

Received: December 9, 2010 Accepted: January 6, 2011 doi:10.5539/jmr.v3n2p126 Abstract Stepwise local error control using local extrapolation in Runge-Kutta methods is well-known. In this paper, we introduce an algorithm, designated RKrvQz, that is capable of controlling local and global errors in a stepwise manner. The algorithm utilizes three Runge-Kutta methods, of orders r, v and z, with r ...

In this report, we restate the problem of maximizing the average quality of an anytime contract algorithm over a time interval, then we present general results about any kind of quality function approximated by a stepwise function. We prove that the problem is NP-hard, but becomes quadratic if the time interval is large enough. This report is associated to the paper published in IJCAI'99 [2] an...

Program slicing can be ffectively used to debug, test, analyze, understand and maintain object-oriented software. In this paper, a new slicing model is proposed to slice Java programs based on their inherent hierarchical feature. The main idea of hierarchical slicing is to slice programs in stepwise way, from package level, to class level, method level, and finally up to st atement level. Stepw...

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al. (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this meth...