In this paper, we present our method which is a performance improvement to the Directed Acyclic Graph Support Vector Machines (DAG SVM). It suggests a weighted multi-class classification technique which divides the input space into several subspaces. In the training phase of the technique, for each subspace, a DAG SVM is trained and its probability density function (pdf) is guesstimated. In the...

Given a simple directed graph D = (V,A), let the size of the largest induced directed acyclic graph (dag) be denoted by mas(D). Let D ∈ D(n, p) be a random instance, obtained by choosing each of the ( n 2 ) possible undirected edges independently with probability 2p and then orienting each chosen edge independently in one of two possible directions with probabibility 1/2. We obtain improved bou...

AbsrructAs the number of available multiprocessors increases, so does the importance of providing software support for these systems, including parallel compilers. Data flow analysis, an important component of software tools, may be computed many times during the compilation of a program, especially when compiling for a multiprocessor. Although converting a sequential data flow algorithm to a p...

The essence of the Bayesian network model is largely embodied by its structural component: a directed acyclic graph (DAG). A DAG encodes assertions of conditional independence, whereas the remaining model parameters specify the actual local conditional distributions. The DAG plays an important role, for instance, in causal discovery and inference, where the DAG is interpreted as a representatio...

In the last decade, scheduling of Directed Acyclic Graph (DAG) application in the context of Grid environment has attracted attention of many researchers. However, deployment of Grid environment requires skills, efforts, budget, and time. Although various simulation toolkits or frameworks are available for simulating Grid environment, either they support different possible studies in Grid compu...

We demonstrate Dagster, a system that implements new approach to scheduling interdependent (Boolean) SAT search activities in high-performance computing (HPC) environments. Our takes as input set of disjunctive clauses (i.e., DIMACS CNF) and labelled directed acyclic graph (DAG) structure describing how the are decomposed into interrelated problems. Component problems solved using standard syst...

Abstract Recursive systems of linear regressions is a consolidated methodology for mediation analysis, allowing to determine causal effects interest in closed form based on the regression coefficients. In dynamic perspective, distributed-lags can be added each order represent persisting over several periods. However, analysis case challenging, because depend time lag, and general procedure comp...

We consider the joint sparse estimation of regression coefficients and covariance matrix for covariates in a high-dimensional model, where predictors are both relevant to response variable interest functionally related one another via Gaussian directed acyclic graph (DAG) model. DAG models introduce sparsity Cholesky factor inverse matrix, pattern turn corresponds specific conditional independe...

Max-Cost Paths in DAGS Let G = (V,E) be a weighted directed graph. Dijkstra’s algorithm constructs a shortest path tree rooted at (any vertex) s in O(|E| log |V |) time (or O(|E|+ |V | log |V |) with more sophisticated data structures) as long as the algorithm does not contain any negative cycles. Surprisingly, the corresponding max-cost problem, i.e., finding a maximum cost simple path tree ro...

In order to improve the performance of Data Mining applications, an effective method is task parallelization. The scheduler on Grid plays an important role to management subtasks so as to achieve high performance. We introduce an additional component that we call serializer, whose purpose is to decompose the tasks into a series of independent tasks according the directed acyclic graph (DAG), an...