We address robust versions of combinatorial optimization problems, focusing on the uncorrelated ellipsoidal uncertainty case, which corresponds to so-called mean-variance optimization. We present a branch and bound-algorithm for such problems that uses lower bounds obtained from Lagrangean decomposition. This approach allows to separate the uncertainty aspect in the objective function from the ...

Classifier decision fusion has been shown to act in a manner analogous to the back-projection of Radon transformations when individual classifier feature sets are non or partially overlapping. It is possible, via this analogy, to demonstrate that standard linear classifier fusion introduces a morphological bias into the decision space due to the implicit angular undersampling of the feature sel...

We study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the overall error and its decomposed contributions. These parameters are the maximum program size, the initial population, and the function set us...

This paper shows that unexpected stock returns must be associated with changes in expected future dividends or expected future returns A vector autoregressive method is used to break unexpected stock returns into these two components. In U.S. monthly data in 1927-88, one-third of the variance of unexpected returns is attributed to the variance of changing expected dividends, one-third to the va...

This paper shows that unexpected stock returns must be associated with changes in expected future dividends or expected future returns A vector autoregressive method is used to break unexpected stock returns into these two components. In U.S. monthly data in 1927-88, one-third of the variance of unexpected returns is attributed to the variance of changing expected dividends, one-third to the va...

We present a bias variance decomposition of expected misclassi cation rate the most commonly used loss function in supervised classi cation learning The bias variance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms yet no decomposition was o ered for the more commonly used zero one misclassi cation loss functions until t...

We offer a decomposition for the variance of current unemployment rate that not only measures contributions labour market flows but also approximation error embedded in other decompositions use surrogates rate. Using data United States and Brazil, results latter show significant differences flows’ non-negligible distortions errors when (instead proxy) is decomposed; U.S., no substantial changes...