object

svr_regression

Support vector regression (SVR) using an epsilon-insensitive dual margin model with linear, polynomial, and radial basis function kernels, trained with a Pegasos-style dual subgradient descent loop. Learns from a dataset object implementing the regression_dataset_protocol protocol and returns a regressor term that can be used for prediction and exported as predicate clauses.

Availability:

logtalk_load(svr_regression(loader))

Author: Paulo Moura

Version: 1:0:0

Date: 2026-08-09

Compilation flags:

static, context_switching_calls

Imports:

public regressor_common

Uses:

format

linear_algebra

list

pairs

type

Remarks:

(none)

Inherited public predicates:

 check_option/1  check_options/1  check_regressor/1  default_option/1  default_options/1  diagnostic/2  diagnostics/2  export_to_clauses/4  export_to_file/4  learn/2  learn/3  option/2  option/3  predict/3  print_regressor/1  regressor_options/2  valid_option/1  valid_options/1  valid_regressor/1

Public predicates

(no local declarations; see entity ancestors if any)

Protected predicates

(no local declarations; see entity ancestors if any)

Private predicates

(no local declarations; see entity ancestors if any)

Operators

(none)

See also

regression_dataset_protocol, kernel_svm_classifier, linear_svm_classifier, linear_regression, ridge_regression, lasso_regression