| sits_svm {sits} | R Documentation |
This function receives a tibble with a set of attributes X for each observation Y. These attributes are the values of the time series for each band. The SVM algorithm is used for multiclass-classification. For this purpose, it uses the "one-against-one" approach, in which k(k-1)/2 binary classifiers are trained; the appropriate class is found by a voting scheme. This function is a front-end to the "svm" method in the "e1071" package. Please refer to the documentation in that package for more details.
sits_svm( data = NULL, formula = sits_formula_logref(), scale = FALSE, cachesize = 1000, kernel = "radial", degree = 3, coef0 = 0, cost = 10, tolerance = 0.001, epsilon = 0.1, cross = 10, ... )
data |
Time series with the training samples. |
formula |
Symbolic description of the model to be fit. (default: sits_formula_logref). |
scale |
Logical vector indicating the variables to be scaled. |
cachesize |
Cache memory in MB (default = 1000). |
kernel |
Kernel used in training and predicting. options: "linear", "polynomial", "radial", "sigmoid" (default: "radial"). |
degree |
Exponential of polynomial type kernel (default: 3). |
coef0 |
Parameter needed for kernels of type polynomial and sigmoid (default: 0). |
cost |
Cost of constraints violation (default: 10. |
tolerance |
Tolerance of termination criterion (default: 0.001). |
epsilon |
Epsilon in the insensitive-loss function (default: 0.1). |
cross |
Number of cross validation folds applied to assess the quality of the model (default: 10). |
... |
Other parameters to be passed to e1071::svm function. |
Model fitted to input data
(to be passed to sits_classify)
Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br
Rolf Simoes, rolf.simoes@inpe.br
Gilberto Camara, gilberto.camara@inpe.br
## Not run:
# Retrieve the set of samples for Mato Grosso (provided by EMBRAPA)
samples_2bands <- sits_select(samples_modis_4bands, bands = c("NDVI", "EVI"))
# Build a machine learning model
ml_model <- sits_train(samples_2bands, sits_svm())
# get a point and classify the point with the ml_model
point.tb <- sits_select(point_mt_6bands, bands = c("NDVI", "EVI"))
class.tb <- sits_classify(point.tb, ml_model)
plot(class.tb, bands = c("NDVI", "EVI"))
## End(Not run)