Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In ...

Re-ranking algorithms have been proposed to improve the effectiveness of content-based image retrieval systems by exploiting contextual information encoded in distance measures and ranked lists. In this paper, we show how ...

The objective of this study was to estimate genetic parameters for 305-day cumulative milk yield (MY305) and its association with test-day milk yield (TDMY) in Saanen and Alpine goats in order to provide information that ...

Objective The aim of this study was to evaluate the dental socket bone healing process by histomorphometric and immunohistochemical analysis of osteoprotegerin (OPG), receptor activator of nuclear factor-kappa beta (RANK), ...

Despite the continuous development of features and mid-level representations, effectively and reliably measuring the similarity among images remains a challenging problem in image retrieval tasks. Once traditional measures ...

This paper introduces the concept of common deterministic shifts (CDS). This concept is simple, intuitive and relates to the common structure of shifts or policy interventions. We propose a Reduced Rank technique to ...

The task of learning to rank has been widely studied by the machine learning community, mainly due to its use and great importance in information retrieval, data mining, and natural language processing. Therefore, ranking ...

The task of learning to rank has been widely studied by the machine learning community, mainly due to its use and great importance in information retrieval, data mining, and natural language processing. Therefore, ranking ...

Recent literature in performance evaluation has focused on preferences and characteristics of returns' distribution that go beyond mean and variance world. However, Eling (2008) compared the Sharpe ratio with some of these ...