plant biomass is an important factor for determining arid and semi-aridrangelands capacity. due to the lack of proper and annual sampling of rangelands, there areno suitable data to determine biomass, range condition and proper range managementoperations. plant biomass is one of the measurable attributes that can be assessed inrangeland studies. since the clip and weight method is destructive a...

Regularized logistic regression is a very successful classification method, but for large-scale data, its distributed training has not been investigated much. In this work, we propose a distributed Newton method for training logistic regression. Many interesting techniques are discussed for reducing the communication cost. Experiments show that the proposed method is faster than state of the ar...

Linear models are often built to understand how a set of input data affects output data and predict its value. A linear regression model assumes that that regression function E(Y |X) is linear in the inputsX1, . . . ,Xp. The output is represented as a weighted sum of the input variables (or features), where the weights are called the regression coefficients. The regressions coefficients are asc...

Geospatial databases generally consist of measurements related to points (or pixels in the case of raster data), lines, and polygons. In recent years, the size and complexity of these databases have increased signiicantly and they often contain erroneous measurements or noise. In this paper, we address the problem of detecting erroneous and suspicious values in a database consisting of point me...

The bioinetrical treatment of laboratory data may require the estimation of a regression line for the transformation of one set of measurements to another. The regression procedure introduced in part I (1) of our work does not always yield unbiased results in such situations, since its estimators are not scale invariant. In part III we present the parameter estimation of a general regression eq...

The present research work outlines the main ideas behind statistical regression by a 2-independent-variates and 1-dependent-variate model based on the invariance of measures in probabilistic spaces. The principle of probabilistic measure invariance, applied under the assumption that the model be isotonic, leads to a system of differential equations. Such differential system is reformulated in t...