sits (version 1.1.0)

sits_svm: Train support vector machine models

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(
  samples = NULL,
  formula = sits_formula_linear(),
  scale = FALSE,
  cachesize = 1000,
  kernel = "radial",
  degree = 3,
  coef0 = 0,
  cost = 10,
  tolerance = 0.001,
  epsilon = 0.1,
  cross = 10,
  ...
)

Value

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

Arguments

samples

Time series with the training samples.

formula

Symbolic description of the model to be fit. (default: sits_formula_linear).

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.

Author

Alexandre Ywata de Carvalho,

Rolf Simoes,

Gilberto Camara,

Examples

if (sits_run_examples()) {
    # Example of training a model for time series classification
    # Retrieve the samples for Mato Grosso
    # train an SVM model
    ml_model <- sits_train(samples_modis_4bands, ml_method = sits_svm)
    # select the bands to classify the point
    sample_bands <- sits_bands(samples_modis_4bands)
    point_4bands <- sits_select(point_mt_6bands, bands = sample_bands)
    # classify the point
    point_class <- sits_classify(point_4bands, ml_model)
    plot(point_class)
}