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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
PARAMETER ESTIMATION PROBLEMS
, 2010
"... Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for lowdimensional robust regression problems. However, the combinatorial nature of these algorithms makes them practically unusable for highdimensional applications. In this paper, we ..."
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which is, in general, a combinatorial problem. We present experimental results that demonstrate the efficacy of the proposed algorithms. We also analyze the intrinsic parameter space of robust regression and identify an efficient and accurate class of algorithms for different operating conditions
PrimalDual Formulations for Parameter Estimation Problems
, 1997
"... A new method for formulating and solving parameter estimation problems based on Fenchel duality is presented. The partial differential equation is considered as a contraint in a least squares type formulation and is realized as a penalty term involving the primal and dual energy functionals associat ..."
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Cited by 5 (0 self)
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A new method for formulating and solving parameter estimation problems based on Fenchel duality is presented. The partial differential equation is considered as a contraint in a least squares type formulation and is realized as a penalty term involving the primal and dual energy functionals
Parameter Estimation Problems in Chemical Equilibrium Analysis
 Linkoping University
, 1994
"... In basic research in Solution Chemistry the formation of complexes in unknown systems are studied at equilibrium. The chemists want to determine which complexes (species) are formed and their formation constants. Using a systematic series of experiments they gather experimental data using electrodes ..."
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Cited by 2 (2 self)
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electrodes and several types of spectroscopical methods, e.g. NMR (Nuclear Magnetic Resonance) and Spectrophotometry. Mathematically we can formulate a number of parameter estimation problems. The problems are large, sparse, illconditioned non linear least squares problems with a special structure
A Multigrid Method For Distributed Parameter Estimation Problems
 Trans. Numer. Anal
, 2001
"... . This paper considers problems of distributed parameter estimation from data measurements on solutions of partial differential equations (PDEs). A nonlinear least squares functional is minimized to approximately recover the sought parameter function (i.e., the model). This functional consists of a ..."
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Cited by 45 (13 self)
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. This paper considers problems of distributed parameter estimation from data measurements on solutions of partial differential equations (PDEs). A nonlinear least squares functional is minimized to approximately recover the sought parameter function (i.e., the model). This functional consists
Nonlinearity, scale and sensitivity for parameter estimation problems
 SIAM J. Sci. Comput
"... Both sensitivity and nonlinearity are important for the efficiency of an estimation algorithm. Knowledge of a general nature on sensitivity and/or nonlinearity for some class of models can perhaps be utilized to improve the estimation efficiency for this class. For an ODE model, a correlation betwee ..."
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Cited by 2 (0 self)
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Both sensitivity and nonlinearity are important for the efficiency of an estimation algorithm. Knowledge of a general nature on sensitivity and/or nonlinearity for some class of models can perhaps be utilized to improve the estimation efficiency for this class. For an ODE model, a correlation
An Iterative Linearised Solution to the Sinusoidal Parameter Estimation Problem
"... Xiph.Org Foundation Signal processing applications use sinusoidal modelling for speech synthesis, speech coding, and audio coding. Estimation of the model parameters involves nonlinear optimisation methods, which can be very costly for realtime applications. We propose a lowcomplexity iterative m ..."
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Xiph.Org Foundation Signal processing applications use sinusoidal modelling for speech synthesis, speech coding, and audio coding. Estimation of the model parameters involves nonlinear optimisation methods, which can be very costly for realtime applications. We propose a lowcomplexity iterative
Calibration and Rescaling Principles for Nonlinear Inverse Heat Conduction and Parameter Estimation Problems
, 2015
"... I am submitting herewith a dissertation written by Yinyuan Chen entitled "Calibration and Rescaling Principles for Nonlinear Inverse Heat Conduction and Parameter Estimation Problems. " I have examined the final electronic copy of this dissertation for form and content and recommend that i ..."
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I am submitting herewith a dissertation written by Yinyuan Chen entitled "Calibration and Rescaling Principles for Nonlinear Inverse Heat Conduction and Parameter Estimation Problems. " I have examined the final electronic copy of this dissertation for form and content and recommend
COMPUTATION OF COVARIANCE MATRICES FOR CONSTRAINED PARAMETER ESTIMATION PROBLEMS USING LSQR ∗
"... Abstract. We consider large parameter estimation problems with nonlinear equality constraints. Each GaussNewton iteration requires the solution of a linear leastsquares problem with linear constraints. We describe the ideal numerical method based on QR factors of the constraint matrix, and show ho ..."
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Abstract. We consider large parameter estimation problems with nonlinear equality constraints. Each GaussNewton iteration requires the solution of a linear leastsquares problem with linear constraints. We describe the ideal numerical method based on QR factors of the constraint matrix, and show
Results 1  10
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163,980