Clustering large data is a fundamental problem with a vast number of applications. Due to the increasing size of data, practitioners interested in clustering have turned to distributed computation methods. In this work, we consider the widely used kcenter clustering problem and its variant used to handle noisy data, k-center with outliers. In the noise-free setting we demonstrate how a previous...

In this paper, we propose an energy-efficient mathematical model for the resource-constrained project scheduling problem to optimize makespan and consumption of energy, simultaneously. In the proposed model, resources are speed-scaling machines. The problem is NP-hard in the strong sense. Therefore, a multi-objective fruit fly optimization algorithm (MOFOA) is developed. The MOFOA uses the VIKO...

Hierarchical clustering has been widely used in numerous applications due to its informative representation of clustering results. But its higher computation cost and inherent data dependency prohibits it from performing on large datasets efficiently. In this paper, we present a distributed singlelinkage hierarchical clustering algorithm (DiSC) based on MapReduce, one of the most popular progra...

We present a subspace-based variant of LS-SVMs (i.e. regularization networks) that sequentially processes the data and is hence especially suited for online learning tasks. The algorithm works by selecting from the data set a small subset of basis functions that is subsequently used to approximate the full kernel on arbitrary points. This subset is identified online from the data stream. We imp...

We propose an efficient algorithm to compute the smoothed correlogram for the detection of temporal relationship between two spike trains. Unlike the conventional histogram-based correlogram estimations, the proposed algorithm operates on continuous time and does not bin either the spike train nor the correlogram. Hence it can be more precise in detecting the effective delay between two recordi...

background: the aim of this work was to study the feasibility of constructing a fast thorax model suitable for simulating lung motion due to respiration using only one ct dataset. materials and methods: for each of six patients with different thorax sizes, two sets of ct images were obtained in single-breath-hold inhale and exhale stages in the supine position. the ct images were then analyzed ...

Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) problem. This paper proposes an algorithm for training SVMs: Sequential Minimal Optimization, or SMO. SMO breaks the large QP problem into a series of smallest possible QP problems which are analytically solvable. Thus, SMO does not require a numerical QP library. SMO’s computation time is d...

‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time...