This predictive analytic model prioritizes high-cost individuals for whom the solution of housing costs less than the problem of homelessness. Cost offsets from reduced service use after high-cost individuals are stably housed can be stretched across a larger pool of homeless people whose...

We set out in this study to review a vast amount of recent literature on machine learning (ML) approaches to predicting financial distress (FD), including supervised, unsupervised and hybrid supervised-unsupervised learning algorithms. Four supervised ML models including the traditional support...

Supervised Machine Learning (SML) is the quest for algorithms that explanation from remotely provided occurrences to deliver general speculations, which then make predictions about future instances.The present exploration investigates the loyalty prediction issue of a brand through supervised...

We present the methodology for developing a predictive model for identifying homeless persons likely to have high future costs for public services. It was developed by linking administrative records from 2007 through 2012 for seven Santa Clara County agencies and identifying 38 demographic,...

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

Accurate forecasting of inter-urban traffic flow has been one of most important issues in the research on road traffic congestion. The traffic flow forecasting involves a rather complex nonlinear data pattern. Recently, support vector regression (SVR) model has been widely used to solve...

Support vector machines (SVMs) have been successfully employed to solve non-linear regression and time series problems. However, SVMs have rarely been applied to forecasting software reliability. This investigation elucidates the feasibility of the use of SVMs to forecast software reliability....