Estimating above-ground biomass of Melaleuca quinquenervia in Florida, USA

One hundred and thirty-eight Melaleuca quinquenervia (Cav.) S. T. Blake (broad-leaved paperbark) trees were harvested from six sites in South Florida to formulate regression equations for estimating tree above-ground dry weight.

A Simplified Evaluation Procedure of the Scale of Fluctuation Combined Recurrence Averaging and Weighted Linear Regression

The divergence of properties from one location to another within a soil mass is termed spatial variability, which traditionally includes three parameters the mean, the standard deviation, and the scale of fluctuation, in order to stochastically describe a soil property. Among them, determining the scale of fluctuation in the evaluation of spatial variability of soil profiles is not easy due to soil condition complexity. A simplified procedure is presented in the paper to determine the scale of fluctuation combined recurrence averaging and weighted linear regression. The alternative approach utilizes widely usable spreadsheet to solve the problem more directly and efficiently.

The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.