D.R. Fulkerson 1976] described a two-phase greedy algorithm to nd a minimum cost spanning arborescence and to solve the dual linear program. This was extended by the present author for "kernel systems", a model including the rooted edge-connectivity augmentation problem, as well. A similar type of method was developed by D. Kornblum 1978] for "lattice polyhedra", a notion introduced by A. Hooma...

Many formalisms have been proposed over the years to capture combinatorial optimization algorithms such as dynamic programming, branch and bound, and greedy. In 1989 Helman presented a common formalism that captures dynamic programming and branch and bound type algorithms. The formalism was later extended to include greedy algorithms. In this paper, we describe the application of automated reas...

this paper explores the flexile flow lines where setup times are sequence- dependent. the optimization criterion is the minimization of total weighted completion time. we propose an iterated greedy algorithm (iga) to tackle the problem. an experimental evaluation is conducted to evaluate the proposed algorithm and, then, the obtained results of iga are compared against those of some other exist...

Recently, we have proposed an unsupervised filterbank learning model based on Convolutional RBM (ConvRBM). This model is able to learn auditory-like subband filters using speech signals as an input. In this paper, we propose two-layer Unsupervised Deep Auditory Model (UDAM) by stacking two ConvRBMs. The first layer ConvRBM learns filterbank from speech signals and hence, it represents early aud...

Several change metrics and source code metrics have been introduced and proved to be effective in bug prediction. Researchers performed comparative studies of bug prediction models built using the individual metrics as well as combination of these metrics. In this paper, we investigate the impact of feature selection in bug prediction models by analyzing the misclassification rates of these mod...

Orthogonal projections are a standard technique of dimensionality reduction in machine learning applications. We study the problem approximating orthogonal matrices so that their application is numerically fast and yet accurate. find an approximation by solving optimization over set structured matrices, we call extended Givens transformations, including rotations as special case. propose effici...