We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Rather than treating the most general case, we focus on two key applications that exemplify our methods: On-line learning with a simple square loss and finding a symmetric positiv...

A key problem in structured output prediction is direct optimization of the task reward function that matters for test evaluation. This paper presents a simple and computationally efficient approach to incorporate task reward into a maximum likelihood framework. We establish a connection between the log-likelihood and regularized expected reward objectives, showing that at a zero temperature, t...

We present a margin-based bound for ranking in a general setting, using the L∞ covering number of the hypothesis space as our complexity measure. Our bound suggests that ranking algorithms that maximize the ranking margin will generalize well. We produce a Smooth Margin Ranking algorithm, which is a modification of RankBoost analogous to Approximate Coordinate Ascent Boosting. We prove that thi...

The exponentiated half-logistic distribution has various shapes depending on its shape parameter. Therefore, this paper proposes more efficient approach methods for estimating shape parameters in the presence of a nuisance parameter, that is, a scale parameter, from Bayesian and non-Bayesian perspectives if record values have an exponentiated half-logistic distribution. In the frequentist appro...

In a recent paper by Tang, Reed and Wagener (2006, hereafter referred to as TRW) a comparison assessment was presented of three state-of-the-art evolutionary algorithms for multiobjective calibration of hydrologic models. Through three illustrative case studies, TRW demonstrate that the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Epsilon Dominance Nondominated Sorted Genetic Algorithm ...

This article introduces a new family of Generalized Exponentiated Exponential distribution. Using the T-R{Y} framework, T-Exponentiated Exponential{Y} distributions named Exponential{Frechet} is proposed. Some general properties such as hazard rate function, quantile non-central moment, mode, mean absolute deviations and Shannon’s entropy are discussed. A continuous univariate probability distr...

Cancer arises from successive rounds of mutations, resulting in tumor cells with different somatic mutations known as clones. Drug responsiveness and therapeutics of cancer depend on the accurate detection of clones in a tumor sample. Recent research has considered inferring clonal composition of a tumor sample using computational models based on short read data of the sample generated using ne...