A Stepwise Regression Algorithm for Prognostication Draft Requirements of Disk Plough

A comparison of the performance of a high speed disk plough and a general purpose mouldboard plough

Stepwise regression for unsupervised learning

I consider unsupervised extensions of the fast stepwise linear regression algorithm [5]. These extensions allow one to efficiently identify highly-representative feature variable subsets within a given set of jointly distributed variables. This in turn allows for the efficient dimensional reduction of large data sets via the removal of redundant features. Fast search is effected here through th...

Reducing quadratic programming problem to regression problem: Stepwise algorithm

Quadratic programming is concerned with minimizing a convex quadratic function subject to linear inequality constraints. The variables are assumed to be nonnegative. The unique solution of quadratic programming (QP) problem (QPP) exists provided that a feasible region is non-empty (the QP has a feasible space). A method for searching for the solution to a QP is provided on the basis of statisti...

Comments on NIST Draft Requirements and Criteria for Hash Algorithm

X-F2-Envelope-From: [email protected] X-F2-Envelope-To: [email protected] Date: Fri, 27 Apr 2007 17:52:43 -0400 From: Eran Tromer User-Agent: Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.8.0.10) Gecko/20070302 Fedora/1.5.0.10-1.fc6 pango-text Thunderbird/1.5.0.10 Mnenhy/0.7.5.0 To: [email protected] CC: Ron Rivest , Ran Canetti <canetti@csail....

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM AND STEPWISE REGRESSION FOR COMPRESSIVE STRENGTH ASSESSMENT OF CONCRETE CONTAINING METAKAOLIN

In the current study two methods are evaluated for predicting the compressive strength of concrete containing metakaolin. Adaptive neuro-fuzzy inference system (ANFIS) model and stepwise regression (SR) model are developed as a reliable modeling method for simulating and predicting the compressive strength of concrete containing metakaolin at the different ages. The required data in training an...