Results 221 to 230 of about 1,048,571 (242)

Linear combination of transformations

Proceedings of the 29th annual conference on Computer graphics and interactive techniques, 2002
Geometric transformations are most commonly represented as square matrices in computer graphics. Following simple geometric arguments we derive a natural and geometrically meaningful definition of scalar multiples and a commutative addition of transformations based on the matrix representation, given that the matrices have no negative real eigenvalues.
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Combining generalized linear models

2018
x CHAPTER 1. GENERAL INTRODUCTION 1 1.1 Model Selection and Model Uncertainty 1 1.2 Model Averaging/Mixing 6 1.3 Adaptive Regression by Mixing 11 1.3.1 The Relationship between ARM and Information Theory 12 1.3.2 Computational Issues 13 1.3.3 Overview of Thesis 14 CHAPTER 2. COMBINING LOGLINEAR MODELS 16 2.
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Zeros of Linear Combinations of Polynomials

Canadian Mathematical Bulletin, 1972
The following theorem is due to J. L. Walsh (see [2, Theorem 17, 2a]):Theorem. If all the zeros of f1(z)=zn+a1zn-1+ … + an lie in or on the circle C1 with centre c1 and radius r1 and if all the zeros of f2(z)=zn+b1zn-1+ … + bn lie in or on the circle C2 with centre c2 and radius r2, then each zero of the polynomiallies in at least one of the circles Γk
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Experiments on Linear Combiners

2008
The Multiple Classifier Systems are nowadays one of the most promising directions in pattern recognition. There are many methods of decision making by the group of classifiers. The most popular are methods that have their origin in vote methods, where the decision of the common classifier is a combination of simple classifiers decisions.
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The Linear Combination Method

2012
In data fusion, the linear combination method is a very flexible method since different weights can be assigned to different systems. When using the linear combination method, how to decide weights is a key issue. Profitable weights assignment is affected by a few factors mainly including performance of all component results and similarity among ...
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Linear Combination and Linear Independence

2021
Rita Fioresi, Marta Morigi
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Optimal Linear Combinations of Neural Networks

Neural Networks, 1997
Neural network-based modeling often involves trying multiple networks with different architectures and training parameters in order to achieve acceptable model accuracy. Typically, one of the trained networks is chosen as best, while the rest are discarded.
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Symmetry-adapted linear combinations

1991
In Chapter 6, it was shown how to determine which irreducible representations were present in any reducible representation. For example, a set of bond vectors for a square-planar (D4h) molecule reduced to A1g + B1g + Eu. What is the physical significance of this result?
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Linear combination

2001
Saul I. Gass, Carl M. Harris
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Linear combinations

1988
Bernhard Flury, Hans Riedwyl
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