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Data mining involves the systematic analysis of large data sets , and data mining in agricultural soil datasets is exciting and modern research area. The productive capacity of...
We develop a highly scalable optimization method called “hierarchical group-thresholding”for solving a multi-task regression model with complex structured sparsity constraints on both input and output...
A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-pass...
We develop and analyze stochastic optimization algorithms for problems in which the ex-pected loss is strongly convex, and the optimum is (approximately)sparse. Previous approaches are able to exploit...
We are presenting a simple and numerical stable algorithm for the solution of the cone projection problem which is suitable for relative small data sets and for simulation purposes needed for con-vexi...
The estimation of small probabilities of failure from computer simulations is a classical problem in engineering, and the Subset Simulation algorithm proposed by Au & Beck (Prob. Eng. Mech., 2001) has...
The PC algorithm uses conditional independence tests for model selection in graphical modeling with acyclic directed graphs. In Gaussian mod-els, tests of conditional independence are typically based ...
We analyse the properties of the Principal Fitted Components (PFC) algorithm proposed by Cook. We derive theoretical properties of the resulting estimators, including sufficient conditions under which...

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