Most of the previous research on term weighting for information retrieval has focused on developing specialized parametric term weighting functions. Examples include TF.IDF vector-space formulations, BM25, and language modeling weighting. Each of these term weighting functions takes on a specific parametric form. While these weighting functions have proven to be highly effective, they impose st...

This paper is a principal idea of case-based reasoning to feature weighting. The feature weighting method called CaDFeW (CAse-based Dynamic FEature Weighting) stores classification performance of randomly generated feature weight vectors. Also it retrieve similar feature weighting success story from the feature weighting case base and then designs a better feature weight vector dynamically for ...

Figure 1 shows the comparison of a general normal based weighting function and the proposed weighting function. They are rendered with voxel cone tracing [Crassin et al. 2011] and spatio-temporal upsampling [Herzog et al. 2010]. The scene has a dynamic object (a galloping horse). In this experiment, the normal based weighting function has a Gaussian distribution [Yang et al. 2008] and its varia...

Researchers have identified hundreds of factors that purport to predict equity returns; we find a half dozen that provide an opportunity to outperform the market. To maximize risk-adjusted returns, diversify across smart beta strategies that access the value, low beta, profitability, investment, momentum, and size factors. Systematic rebalancing to fixed weights—reducing exposure to popular fac...

In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors the problem data, given information operation time and setup costs. We propose...