Convergence of the Iterative Proportional Fitting procedure is analyzed. The input comprises a nonnegative weight matrix, and positive target marginals for rows and columns. The output sought is what is called the biproportional fit, a scaling of the input weight matrix through row and column divisors so as to equate row and column sums to target marginals. The procedure alternates between the fitting of rows, and the fitting of columns. We monitor progress with an L1-error function measuring the distance between current row and column sums and target row and column marginals. The procedure converges to the biproportional fit if and only if the L1-error tends zero. In case of non-convergence the procedure appears to oscillate between two ac...
Iterative proportional fitting (IPF) is a calibration technique for estimating cell frequencies of a...
This paper proves continuity of f-projections and the continuous dependence of the limit matrix of t...
AbstractWe study in detail the behavior of some known learning algorithms. We estimate the sum of th...
Convergence of the Iterative Proportional Fitting procedure is analyzed. The input comprises a nonne...
Convergence of the Iterative Proportional Fitting procedure is analyzed. The input comprises a nonne...
A new analysis of the Iterative Proportional Fitting procedure is presented. The input data consist ...
A new analysis of the Iterative Proportional Fitting procedure is presented. The input data consist ...
The asymptotic behavior of the iterative proportional fitting procedure (IPF procedure) is analyzed ...
In this paper we give a proof of convergence of the iterative proportional fitting procedures (IPFP)...
The iterative proportional fitting procedure (IPF procedure) alternately fits a given nonnegative ma...
International audienceThe iterative proportional fitting procedure (IPFP), introduced in 1937 by Kru...
Iterative proportional fitting (IPF) is described formally and historically and its advantages and l...
This thesis is being archived as a Digitized Shelf Copy for campus access to current students and st...
<p>Convergence of the coefficients matrices versus iterations (mean and standard deviation) of the ...
We present an iterative procedure which asymptotically scales the infinity norm of both rows and col...
Iterative proportional fitting (IPF) is a calibration technique for estimating cell frequencies of a...
This paper proves continuity of f-projections and the continuous dependence of the limit matrix of t...
AbstractWe study in detail the behavior of some known learning algorithms. We estimate the sum of th...
Convergence of the Iterative Proportional Fitting procedure is analyzed. The input comprises a nonne...
Convergence of the Iterative Proportional Fitting procedure is analyzed. The input comprises a nonne...
A new analysis of the Iterative Proportional Fitting procedure is presented. The input data consist ...
A new analysis of the Iterative Proportional Fitting procedure is presented. The input data consist ...
The asymptotic behavior of the iterative proportional fitting procedure (IPF procedure) is analyzed ...
In this paper we give a proof of convergence of the iterative proportional fitting procedures (IPFP)...
The iterative proportional fitting procedure (IPF procedure) alternately fits a given nonnegative ma...
International audienceThe iterative proportional fitting procedure (IPFP), introduced in 1937 by Kru...
Iterative proportional fitting (IPF) is described formally and historically and its advantages and l...
This thesis is being archived as a Digitized Shelf Copy for campus access to current students and st...
<p>Convergence of the coefficients matrices versus iterations (mean and standard deviation) of the ...
We present an iterative procedure which asymptotically scales the infinity norm of both rows and col...
Iterative proportional fitting (IPF) is a calibration technique for estimating cell frequencies of a...
This paper proves continuity of f-projections and the continuous dependence of the limit matrix of t...
AbstractWe study in detail the behavior of some known learning algorithms. We estimate the sum of th...