Table 2 Overview of binary classification models and corresponding MATLAB functions trained using the input data X (i.e. 20 dimensional multi-color space representation of manually segmented FLU+VIS plant and background regions) and the vector of region labels Y (i.e. Y=0 for background, Y=1 for plant image regions)
# | Classification model | Acronym | MATLAB function |
|---|---|---|---|
1 | Naive Bayes model | bayes | fitcnb(X,Y) |
2 | Discriminant analysis | da | fitcdiscr(X,Y) |
3 | Generalized linear regression | glm | fitglm(X,Y) |
4 | Gaussian process regression | gpr | fitrgp(X,Y) |
5 | Linear model regression | linmod | fitlm(X,Y) |
6 | Binary support vector machine | svm | fitcsvm(X,Y) |
7 | Support vector machine regression | svm | fitrsvm(X,Y) |
8 | Neural network model | net | train(net,X,Y) |
| Â | (e.g., patternnet with N hidden layers) | Â | net=patternnet(N) |