The purpose of this paper is to discuss empirical risk minimization when the losses are not necessarily bounded and may have a distribution with heavy tails. In such situations usual empirical averages may fail to provide reliable estimates and empirical risk minimization may provide large excess risk. However, some robust mean estimators proposed in the literature may be used to replace empiri...

In this note we study lower bounds on the empirical minimization algorithm. To explain the basic set up of this algorithm, let (Ω, μ) be a probability space and set X to be a random variable taking values in Ω, distributed according to μ. We are interested in the function learning (noiseless) problem, in which one observes n independent random variables X1, . . . , Xn distributed according to μ...

This paper studies properties of an estimator of mean-variance portfolio weights in a market model with multiple risky assets and a riskless asset. Theoretical formulas for the mean square error are derived in the case when asset excess returns are multivariate normally distributed and serially independent. The sensitivity of the portfolio estimator to errors arising from the estimation of the ...

the matrix functions appear in several applications in engineering and sciences. the computation of these functions almost involved complicated theory. thus, improving the concept theoretically seems unavoidable to obtain some new relations and algorithms for evaluating these functions. the aim of this paper is proposing some new reciprocal for the function of block anti diagonal matrices. more...

This paper deals with the representation of the family in Japanese TV commercials. Based on empirical research conducted in 2004 and 2005, it argues that Japanese commercials tend to depict the family and its members in highly stereotypical ways. Mothers are almost always shown doing some kind of housework, at times supported by their daughters, preparing for their future role as a mother and w...

We consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsity-inducing norms. These are defined as sums of Euclidean norms on certain subsets of variables, extending the usual l1-norm and the group l1-norm by allowing the subsets to overlap. This leads to a specific set of allowed nonzero patterns for the solutions of such problem...

The main principle of stacked generalization (or Stacking) is using a second-level generalizer to combine the outputs of base classifiers in an ensemble. In this paper, we investigate different combination types under the stacking framework; namely weighted sum (WS), class-dependent weighted sum (CWS) and linear stacked generalization (LSG). For learning the weights, we propose using regularize...

Covariance matrix estimation plays a significant role in both the theory and practice of portfolio analysis risk management. This paper deals with available data prior to developing factor model enhance covariance estimation. Our work has two main outcomes. First, for general linear unknown parameters, class best empirical Bayes estimators is established through kinds architectures improve accu...

In this paper, we develop a randomized algorithm and theory for learning a sparse model from large-scale and high-dimensional data, which is usually formulated as an empirical risk minimization problem with a sparsity-inducing regularizer. Under the assumption that there exists a (approximately) sparse solution with high classification accuracy, we argue that the dual solution is also sparse or...

Leveraging the celebrated support vector regression (SVR) method, we propose a unifying framework in order to deliver regression machines in reproducing kernel Hilbert spaces (RKHSs) with data sparsity. The central point is a new definition of -insensitivity, valid for many regression losses (including quantile and expectile regression) and their multivariate extensions. We show that the dual o...