Mixed Linear Analysis

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mixed linear model (Zhang et al. 2010) implemented in the GAPIT package (Lipka et al. 2012) in R. determines the trade-off between misclassifying training examples and minimizing the norm of the weights. Parameter controls the band of the insensitive zone that in turn affects the number of support vectors in building the regression function. Bigger means lesser support vectors and produces more ‘flat’ estimates. III. A LGORITHM D ESCRIPTION Decision Tree (DT) is used to build regression or classification models in the form of a tree structure. It predicts the value of a target variable based on simple decision rules inferred from the data features. It breaks down the dataset into smaller subsets while concurrently an associated decision…show more content…
For analysis purpose, we used image data acquired on 8 July 2016 while manual measurement of the canopy height was done on 25 July 2016. Coefficient of variation (CV) was found higher for canopy height estimated from image when compared to canopy height from ground truth data. We believe that the higher variation in the table (between manual and image data) is because of the difference in the dates of manual measurement and capturing of images. Eq. 2 ∑ i∈Nm (y i − C m ) 2 Eq. 3 Random forest is a variation of Decision Tree which generates different regression trees at training time and outputs the mean prediction of the individual trees. Support Vector Machines are a particular type of algorithms that uses kernels and provides control on deciding the margin or the number of support vectors. The basic goal is to map nonlinearly the data into a high- dimensional feature space and then a linear model is generated in the feature space. The quality of estimation is measured by the insensitive loss function. High performance accuracy of the model can be achieved by tuning C, kernel and epsilon parameters. Parameter C Fig 4: Correlation of Canopy height taken manually with canopy height extracted from Image data 5B. Growth pattern analysis from
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