In this article, we propose a novel regression method which is based solely on Support Vector Classification. The experiments show that the new method has comparable or better generalization performance than ε-insensitive Support Vector Regression. The tests were performed on synthetic data, on various publicly available regression data sets, and on stock price data. Furthermore, we demonstrate...

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is s...

The problem of multiclass classification is considered and resolved through the multiresponse linear regression approach. Scores are used to encode the class labels into multivariate responses. The regression of scores on input attributes is used to extract a lowdimensional linear discriminant subspace. The classification training and prediction are carried out in this low-dimensional subspace....

This study uses a boundary design and propensity score methods to the effects of 1930s-era Home Owners Loan Corporation (HOLC) “redlining” maps on long-run trajectories urban neighborhoods. The led reduced home ownership rates, house values, rents increased racial segregation in later decades. A comparison either side city-level population cutoff that determined whether were drawn finds broadly...

This paper studies how the impact of monetary policy depends on distribution savings from refinancing mortgages. We show that efficacy is state dependent, varying in a systematic way with pool potential refinancing. construct quantitative dynamic life-cycle model accounts for our findings and use it to study response consumption change mortgage rates These effects are strongly dependent. also l...