sits (version 0.13.0)

sits_svm: Train a sits classification model using a support vector machine

Description

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.

Usage

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 = 0,
  ...
)

Arguments

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.

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.

...

Other parameters to be passed to e1071::svm function.

Value

Model fitted to input data (to be passed to sits_classify)

Examples

# 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"))
# }
# NOT RUN {
# }