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Biometrika, 1998
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Denison, David G. T. +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Denison, David G. T. +2 more
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Information Systems, 1990
We present an algorithm using statistical information about word and digraph frequencies as well as the structural pattern concerning words with a common root and different suffixes. The single or composed most frequent words, included in a suitable dictionary, are encoded with a text pointer; the remaining words, where possible, are encoded according ...
SESSA, Maria Immacolata, PIERRO A.
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We present an algorithm using statistical information about word and digraph frequencies as well as the structural pattern concerning words with a common root and different suffixes. The single or composed most frequent words, included in a suitable dictionary, are encoded with a text pointer; the remaining words, where possible, are encoded according ...
SESSA, Maria Immacolata, PIERRO A.
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SIAM Journal on Scientific and Statistical Computing, 1986
It is shown first that an algebraic Riccati equation: \(G+A^*X+XA- XFX=0_ n\) may be solved by reducing the Hamiltonian matrix \(H=\left[ \begin{matrix} A^*\quad G\\ F\quad -A\end{matrix} \right]\) to a triangular form: \(Q^*HQ=\left[ \begin{matrix} T^*_ 1\\ 0_ n\end{matrix} \begin{matrix} T_ 2\\ - T_ 1\end{matrix} \right]\) where \(T_ 1\) is lower ...
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It is shown first that an algebraic Riccati equation: \(G+A^*X+XA- XFX=0_ n\) may be solved by reducing the Hamiltonian matrix \(H=\left[ \begin{matrix} A^*\quad G\\ F\quad -A\end{matrix} \right]\) to a triangular form: \(Q^*HQ=\left[ \begin{matrix} T^*_ 1\\ 0_ n\end{matrix} \begin{matrix} T_ 2\\ - T_ 1\end{matrix} \right]\) where \(T_ 1\) is lower ...
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A reduced subgradient algorithm
1987The authors describe an iterative method for the approximate solution of nondifferentiable convex programming problems. The problems incorporate linear equality and inequality constraints \((Ax=b\), \(x\geq 0)\); the method combines Wolfe's well-known reduced gradient algorithm with the bundle method of nonsmooth optimization.
Bihain, André +2 more
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A macroion electrokinetics algorithm
The Journal of Chemical Physics, 2006A numerical algorithm is presented for the standard model of macroion electrokinetics and certain generalizations of it. The macroion consists of a cylindrical section with identical, hemispheroidal endcaps, each piece having arbitrary length. The system of one macroion and adjoining salt solution is subjected to an arbitrary sequence of pulsed ...
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A Bayesian Morphometry Algorithm
IEEE Transactions on Medical Imaging, 2004Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables.
Edward H, Herskovits +2 more
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A Heuristic Scheduling Algorithm
Journal of the Operational Research Society, 1980This paper describes a heuristic algorithm developed to schedule a group of individuals such that every person performs each of the different activities they desire at some point during the time-frame of the schedule and the difference between the exogenously given number of people desired at each available location-activity-period position and those ...
Hosios, A. J., Rousseau, J. M.
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SIAM Journal on Scientific and Statistical Computing, 1988
It is well known that the Remes algorithm is an efficient method used in computation. However, the Remes algorithm does not always converge unless certain conditions are satisfied. The author presents a certain property and a modified Remes algorithm. If the initial approximation has the property, the modified Remes algorithm converges.
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It is well known that the Remes algorithm is an efficient method used in computation. However, the Remes algorithm does not always converge unless certain conditions are satisfied. The author presents a certain property and a modified Remes algorithm. If the initial approximation has the property, the modified Remes algorithm converges.
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IEEE Transactions on Neural Networks, 1993
A rule of thumb for obtaining good generalization in systems trained by examples is that one should use the smallest system that will fit the data. Unfortunately, it usually is not obvious what size is best; a system that is too small will not be able to learn the data while one that is just big enough may learn very slowly and be very sensitive to ...
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A rule of thumb for obtaining good generalization in systems trained by examples is that one should use the smallest system that will fit the data. Unfortunately, it usually is not obvious what size is best; a system that is too small will not be able to learn the data while one that is just big enough may learn very slowly and be very sensitive to ...
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