The method of Structural Risk Minimization refers to tuning the capacity of the classifier to the available amount of training data. This capacity is influenced by several factors, including: (1) properties of the input space, (2) nature and structure of the classifier, and (3) learning algorithm. Actions based on these three factors are combined here to control the capacity of linear classifie...

Learning is posed as a problem of function estimation, for which two principles of solution are considered: empirical risk minimization and structural risk minimization. These two principles are applied to two different statements of the function estimation problem: global and local. Systematic improvements in prediction power are illustrated in application to zip-code recognition.

Arguably, the natural gas transmission pipeline infrastructure in Iran represents one of the largest and most complex mechanical systems in the world. The optimization of large gas trunk lines known as IGAT results in reduced fuel consumption or higher capability and improves pipeline operation. In the current study, a single-objective optimization was conducted for Khormoj compressor station o...

We extend the VC theory of statistical learning to data dependent spaces of classifiers. This theory can be viewed as a decomposition of classifier design into two components; the first component is a restriction to a data dependent hypothesis class and the second is empirical risk minimization within that class. We define a measure of complexity for data dependent hypothesis classes and provid...

Given a probability measure P and a reference measure μ, one is often interested in the minimum μ-measure set with P -measure at least α. Minimum volume sets of this type summarize the regions of greatest probability mass of P , and are useful for detecting anomalies and constructing confidence regions. This paper addresses the problem of estimating minimum volume sets based on independent samp...

The rank minimization problem is to find the lowest-rank matrix in a given set. Nuclear norm minimization has been proposed as an convex relaxation of rank minimization. Recht, Fazel, and Parrilo have shown that nuclear norm minimization subject to an affine constraint is equivalent to rank minimization under a certain condition given in terms of the rank-restricted isometry property. However, ...

In this paper, it is shown that by exploiting the explicit parametric state feedback solution, it is feasible to obtain the ultimate solution to minimum sensitivity problem. A numerical algorithm for construction of a robust state feedback in eigenvalue assignment problem for a controllable linear system is presented. By using a generalized parametric vector companion form, the problem of eigen...

By minimizing the total potential energy function and deploying the virtual work principle, a higher-order stiffness matrix is achieved. This new tangent stiffness matrix is used to solve the frame with geometric nonlinear behavior. Since authors’ formulation takes into account the higher-order terms of the strain vector, the convergence speed of the solution process will increase. In fac...

State space minimization techniques are crucial for combating state explosion. A variety of explicitstate verification tools use bisimulation minimization to check equivalence between systems, to minimize components before composition, or to reduce a state space prior to model checking. Experimental results on bisimulation minimization in symbolic model checking contexts, however, are mixed. Th...