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Loss functions are a cornerstone of machine learning and the starting point of most algorithms.
Über die praktische auflösung von linearen integralgleichungen mit anwendungen auf randwertaufgaben der potentialtheorie
Evert Johannes Nyström · 1928
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Rôle et domaine d’application du théorème de Bayes selon les différents points de vue sur les probabilités ( in French
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An empirical distribution function for sampling with incomplete information
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Nonmetric multidimensional scaling: A numerical method
J.B. Kruskal · 1964
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The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
L. M. Bregman · 1967
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Elicitation of personal probabilities and expectations
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A theory of the learnable
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A general method for comparing probability assessors
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W.K. Hardle and Berwin Turlach · 1992
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Quadratic Forms in Random Variables: Theory and Applications
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Claudia Czado and Axel Munk · 2000
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Bayesian variable and link determination for generalised linear models
Ioannis Ntzoufras, Petros Dellaportas, and Jonathan Forster · 2000
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Choosing the link function and accounting for link uncertainty in generalized linear models using bayes factors
Claudia Czado and Adrian Raftery · 2002
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Random point fields associated with certain fredholm determinants ii: Fermion shifts and their ergodic and gibbs properties
Tomoyuki Shirai and Yoichiro Takahashi · 2003
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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J. Zhang · 2004
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