Traditional image classification algorithms are mainly divided into unsupervised and supervised paradigms. In the first paradigm, algorithms are designed to automatically estimate the classes’ distributions in the feature space. The second paradigm depends on the knowledge of a domain expert to identify representative examples from the image to be used for estimating the classification model. R...

Support vector machines (SVMs) are a class of popular classification algorithms for their high generalization ability. However, it is time-consuming to train SVMs with a large set of learning samples. Improving learning efficiency is one of most important research tasks on SVMs. It is known that although there are many candidate training samples in some learning tasks, only the samples near pla...

Generalized Selection Bias and The Decomposition of Wage Differentials The major contribution of this paper is ending a new and flexible way to measure the effects of selection on log-wages. In this context, we offer a general approach to performing decomposition analysis when selection effects are present. We call the difference between unconditional and conditional expectations of the log-wag...

Productivity, Wages, and Marriage: The Case of Major League Baseball Using a sample of professional baseball players from 1871–2007, this paper aims at analyzing a longstanding empirical observation that married men earn significantly more than their single counterparts holding all else equal (the “marriage premium”). Baseball is a unique case study because it has a long history of statistics c...

Given the different income inequalities between rural, urban and metropolitan areas in Paraguay, the standard Roy model predicts the possibility that a selection bias of rural metropolitan migrants exists. Based on an extended Roy model, which allows for correlation between labor market characteristics and moving cost, I use a switching regression model to evaluate if migrants from rural to met...