.. _library_kernel_svm_classifier:

kernel_svm_classifier

Kernel support vector machine classifier using one-vs-rest dual margin models with linear, polynomial, and radial basis function kernels. The implementation encodes tabular datasets using the shared linear encoder pipeline so mixed continuous and categorical datasets are handled consistently with the existing linear classifiers.

The library implements the classifier_protocol defined in the classification_protocols library. It provides predicates for learning a classifier from a dataset, using it to make predictions, estimating class probabilities, and exporting it as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the dataset_protocol protocol from the classification_protocols library. Continuous, categorical, and mixed-feature datasets are supported.

API documentation

Open the `../../docs/library_index.html#kernel_svm_classifier <../../docs/library_index.html#kernel_svm_classifier>`__ link in a web browser.

Loading

To load this library, load the loader.lgt file:

::

| ?- logtalk_load(kernel_svm_classifier(loader)).

Testing

To test this library predicates, load the tester.lgt file:

::

| ?- logtalk_load(kernel_svm_classifier(tester)).

Features

• Binary and Multiclass Classification: Learns one-vs-rest dual models and predicts the class with the highest decision score.
• Multiple Kernels: Supports linear, polynomial(Degree, Gamma, Coef0), and rbf(Gamma) kernels.
• Mixed Features: Reuses the shared tabular encoders for continuous and categorical attributes, including missing-value indicators.
• Probability Estimation: Provides class probabilities using a softmax over kernel decision scores.
• Regularized Training: Supports configurable learning-rate scheduling, tolerance, and L2 regularization.
• Classifier Export: Learned classifiers can be exported as predicate clauses or written to a file.

Options

The learn/3 predicate supports these options:

• kernel/1 - kernel function to use (default: linear)
• learning_rate/1 - base learning rate for the dual optimization loop (default: 0.5)
• learning_schedule/1 - learning-rate schedule, either constant or inverse_scaling(Power) (default: constant)
• maximum_iterations/1 - maximum number of optimization epochs (default: 25)
• tolerance/1 - convergence threshold for the maximum parameter update (default: 1.0e-5)
• l2_regularization/1 - L2 penalty factor applied during optimization (default: 0.001)
• feature_scaling/1 - whether to standardize continuous attributes before encoding (default: true)

Usage

Learning a classifier ~~~~~~~~~~~~~~~~~~~~~

::

| ?- kernel_svm_classifier::learn(weather, Classifier).

| ?- kernel_svm_classifier::learn(mixed, Classifier, [kernel(rbf(0.5)), maximum_iterations(50)]).

Making predictions ~~~~~~~~~~~~~~~~~~

::

| ?- kernel_svm_classifier::learn(mixed, Classifier), kernel_svm_classifier::predict(Classifier, [age-35, income-65000, student-yes, credit_rating-fair], Class).

| ?- kernel_svm_classifier::learn(weather, Classifier), kernel_svm_classifier::predict_probabilities(Classifier, [outlook-sunny, temperature-hot, humidity-high, windy-false], Probabilities).

Exporting the classifier ~~~~~~~~~~~~~~~~~~~~~~~~

::

| ?- kernel_svm_classifier::learn(weather, Classifier), kernel_svm_classifier::export_to_clauses(weather, Classifier, classify, Clauses).

| ?- kernel_svm_classifier::learn(weather, Classifier), kernel_svm_classifier::export_to_file(weather, Classifier, classify, 'classifier.pl').

Classifier representation

The learned classifier is represented as a compound term with the form:

::

kernel_svm_classifier(Classes, Encoders, Kernel, TrainingRows, Models, Options)

Where:

• Classes: list of class labels
• Encoders: list of continuous scaling descriptors and categorical value encoders
• Kernel: selected kernel specification
• TrainingRows: encoded feature vectors for the training examples
• Models: list of class_model(Class, Bias, Coefficients) terms
• Options: merged training options used to learn the classifier When exported using export_to_clauses/4 or export_to_file/4, this classifier term is serialized directly as the single argument of the generated predicate clause so that the exported model can be loaded and reused as-is.

References

1. Cortes, C. and Vapnik, V. (1995). "Support-Vector Networks".
2. Scholkopf, B. and Smola, A.J. (2002). "Learning with Kernels".
3. Bishop, C.M. (2006). "Pattern Recognition and Machine Learning". Chapter 7.