We introduce a class of exible conditional probability models and techniques for classi cation regression problems Many existing methods such as generalized linear models and support vector machines are subsumed under this class The exibility of this class of techniques comes from the use of kernel functions as in support vector machines and the generality from dual formulations of stan dard re...

Interpretation of a machine learning induced models is critical for feature engineering, debugging, and, arguably, compliance. Yet, best of breed machine learning models tend to be very complex. This paper presents a method for model interpretation which has the main benefit that the simple interpretations it provides are always grounded in actual sets of learning examples. The method is valida...

Almost all of the current nonparametric regression methods such as smoothing splines, generalized additive models and varying coefficients models assume a linear relationship when nonparametric functions are regarded as parameters. In this article, we propose a general class of nonlinear nonparametric models that allow nonparametric functions to act nonlinearly. They arise in many fields as eit...

Recent articles, such as McCauley-Bell et al. (1999) and Sánchez and Gómez (2003a, 2003b, 2004), used fuzzy regression (FR) in their analysis. Following Tanaka et. al. (1982), their regression models included a fuzzy output, fuzzy coefficients and an nonfuzzy input vector. The fuzzy components were assumed to be triangular fuzzy numbers (TFNs). The basic idea was to minimize the fuzziness of th...

selection of an appropriate model is important for quantifying response of germination rate to temperature and determination of cardinal temperatures. this study was done to evaluate different nonlinear regression models to describe response of germination rate to temperature in medicinal pumpkin, borago and black cumin. the regression models were dent-like, segmented, beta, curvilinear, quadra...

In this paper, we propose a scalar variable formation of fuzzy regression model based on the axiomatic credibility measure foundation. The fuzzy estimation for fuzzy regression coefficients is investigated. A general M-estimation criterion is developed under Maximum Fuzzy Uncertainty Principle, which resulted in weighted Normal equation with adjusted term for M-estimator of the regression coeff...

Sir David Cox’s statistical career and his lifelong interest in the theory and application of stochastic processes began with problems in the wool industry. The problem of drafting a strand of wool yarn to near uniform width is not an auspicious starting point, but an impressive array of temporal and spectral methods from stationary time series were brought to bear on the problem in Cox (1949)....

The European steel industry's workforce is highly heterogeneous and consists of various occupational groups, presumably facing different psychosocial stressors. The few existing studies on the subject mainly focused on physical constraints of blue-collar workers, whereas the supposable psychosocial workload received only little research attention. This is remarkable considering the challenges a...