Data mining and related applications often rely on extensive range sum queries and thus, it is important for these queries to scale well. Range sum queries in data cubes can be achieved in time O(1) using prefix sum aggregates but prefix sum update costs are proportional to the size of the data cube O ( nd ) . Using the Relative Prefix Sum (RPS) method, the update costs can be reduced to the ro...

Data mining and related applications often rely on extensive range sum queries and thus, it is important for these queries to scale well. Range sum queries in data cubes can be achieved in time O(1) using prefix sum aggregates but prefix sum update costs are proportional to the size of the data cube O ( nd ) . Using the Relative Prefix Sum (RPS) method, the update costs can be reduced to the ro...

This study develops and analyzes preconditioned Krylov subspace methods to solve linear systems arising from discretization of the time-independent space-fractional models. First, we apply shifted Grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. Then, we employee two preconditioned iterative methods, namely, the preconditioned gen...

Œe main task in training a linear classi€er is to solve an unconstrained minimization problem. In applying an optimization method to obtain a model, typically we iteratively €nd a good direction and then decide a suitable step size. Past developments of extending optimization methods for large-scale linear classi€cation focus on €nding the direction, but liŠle aŠention has been paid on adjustin...

In this paper we extend the results obtained for a class of finite kernel functions by Y.Q. Bai M. El Ghami and C.Roos published in SIAM Journal of Optimization, 13(3):766–782, 2003 [3] for linear optimization to semidefinite optimization. We show that the iteration bound for primal dual methods is O( √ n log n log n ), for large-update methods andO( √ n log n ), for small-update methods. The i...

The project aims to develop a domain-independent and adaptive approach for scientific document classification using both information fromdocument contents and citation links. We evaluate several content-based classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees and find that the naive Bayes outperform other when training set is sufficiently lar...