Download Fundamentals of Reservoir Engineering by L.P. Dake PDF

By L.P. Dake

I'm a mechanical engineer who has no historical past with geophysics or reservoir engineering, yet i must say that in case you have a few wisdom on thermodynamics , chemistry and a few calculus, this e-book will rather stroll you thru the basics of Reservoir engineering.
It is admittedly thorough and it explains issues in a truly easy way.
Good publication for somebody who desires to learn about this box, and desires to coach herself or himself approximately it.

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K is the number of features in the selected subset. Rcf calculates the average feature-to-class correlation, and Rf f stands for the average feature-to-feature dependence. We adopt the Symmetrical Uncertainty (U) [9] to calculate Rcf and Rf f . This measure is a modified version of the information gain measure which compensates for information gain’s bias toward attributes with more values. 0 × Inf oGain H(Y ) + H(X) where H(Y ) = − p(y) log(p(y)). y∈Y In summary, during view validation, each view is called upon to give a prediction of the target class, against a validation data set.

A new nonlinear classification model based on cross-oriented Choquet integrals. In: Proc. Inter. Conf. Information Science and Technology, Nanjing, China, pp. 176–181 (2011) 7. : A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm. Pattern Recognition 43, 1393–1401 (2010) 8. : UCI Machine Learning Repository. html 9. : Nonlinear Integrals and Their Application on Data Mining. O. no Abstract. Identifying a good feature subset that contributes most to the performance of Lp-norm Support Vector Machines (Lp-SVMs with p = 1 or p = 2) is an important task.

3) Proposition 1: Suppose that S1 and S2 are the minimal values of the objective functions from (1) and (3), respectively. The following inequality is true: S2 ≤ S1 (4) Proof. , 1). Remark 1. As a consequence of the Proposition 1, solving the problem (3) results in a smaller error penalty and enlarges the margin between two support vector hyper-planes, thus possibly giving a better generalization capability of SVM than solving the traditional Lp-norm SVMs in (1). 2. We can even control the sparsity of the general Lp-norm SVMs by adding the following linear constraint x1 + x2 + ..

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