oracle.dmt.jdm.algorithm.svm.classification
Class OraSVMClassificationSettings

java.lang.Object
oracle.dmt.jdm.OraDMObject
oracle.dmt.jdm.base.OraAlgorithmSettings
oracle.dmt.jdm.supervised.OraSupervisedAlgorithmSettings
oracle.dmt.jdm.algorithm.svm.classification.OraSVMClassificationSettings

All Implemented Interfaces:

javax.datamining.base.AlgorithmSettings, oracle.dmt.jdm.OraPLSQLConstants, javax.datamining.supervised.SupervisedAlgorithmSettings, javax.datamining.algorithm.svm.classification.SVMClassificationSettings

public class OraSVMClassificationSettings

extends oracle.dmt.jdm.supervised.OraSupervisedAlgorithmSettings

implements javax.datamining.algorithm.svm.classification.SVMClassificationSettings

OraSVMClassificationSettings defines oracle specific SVM algorithm settings i.e., active learning and outlier rate.

• By default active learning is enabled to limit the size of the model. SVM models grow as the size of the training data set increases. This property limits SVM models to small and medium size training sets (less than 100,000 items). Active learning provides a way to deal with large training sets.
• Outlier rate is used for One-Class SVM models to specify the desired rate of outliers in the training data. By default it is 0.1.

One-Class SVM models are used for anomaly detection. For One-Class SVM, the target must be set to null (the default).

Field Summary

Fields inherited from interface oracle.dmt.jdm.OraPLSQLConstants

abns_max_build_minutes, abns_max_nb_predictors, abns_max_predictors, abns_model_type, abns_multi_feature, abns_naive_bayes, abns_single_feature, algo_adaptive_bayes_network, algo_ai_mdl, algo_ai_mdl2, algo_apriori_association_rules, algo_decision_tree, algo_kmeans, algo_naive_bayes, algo_name, algo_nonnegative_matrix_factor, algo_ocluster, algo_predictor_variance, algo_support_vector_machines, apply_cost_content, apply_nodeid_content, apply_pred_value_content, apply_probability_content, asso_max_rule_length, asso_min_confidence, asso_min_support, association, association_in_model, attribute_importance, clas_cost_table_name, clas_priors_table_name, classification, clus_num_clusters, clustering, feat_num_features, feature_extraction, kmns_block_growth, kmns_conv_tolerance, kmns_cosine, kmns_distance, kmns_euclidean, kmns_fast_cosine, kmns_iterations, kmns_min_pct_attr_support, kmns_num_bins, kmns_size, kmns_split_criterion, kmns_variance, nabs_pairwise_threshold, nabs_singleton_threshold, nmfs_conv_tolerance, nmfs_num_iterations, nmfs_random_seed, ocluster_max_buffer, ocluster_sensitivity, operator_equal, operator_equal_v, operator_greater_or_equal, operator_greater_or_equal_v, operator_greater_than, operator_greater_than_v, operator_in, operator_in_v, operator_less_or_equal, operator_less_or_equal_v, operator_less_than, operator_less_than_v, operator_not_equal, operator_not_equal_v, operator_not_in, operator_not_in_v, oracle_char_type, oracle_dm_nested_categoricals, oracle_dm_nested_numericals, oracle_float_type, oracle_number_type, oracle_varchar2_type, regression, svms_active_learning, svms_al_disable, svms_al_enable, svms_complexity_factor, svms_conv_tolerance, svms_epsilon, svms_gaussian, svms_kernel_cache_size, svms_kernel_function, svms_linear, svms_outlier_rate, svms_std_dev, tree_impurity_entropy, tree_impurity_gini, tree_impurity_metric, tree_impurity_metric_default, tree_term_max_depth, tree_term_max_depth_default, tree_term_max_depth_max, tree_term_max_depth_min, tree_term_max_surrogates_max, tree_term_max_surrogates_min, tree_term_minpct_node, tree_term_minpct_node_default, tree_term_minpct_node_max, tree_term_minpct_split, tree_term_minpct_split_default, tree_term_minpct_split_max, tree_term_minrec_node, tree_term_minrec_node_default, tree_term_minrec_split, tree_term_minrec_split_default

Method Summary

boolean	getActiveLearning() Returns true if active learning by algorithm is enabled, either set by the user or the default value.
double	getOutlierRate() Returns the outlier rate, either set by the user or the default value.
void	setActiveLearning(boolean enable) Sets to true to enable active learning by algorithm, otherwise sets to false.
void	setOutlierRate(double outlierRate) Sets the outlier rate that is a parameter the approximate rate of outliers (negative predictions) produced by a one-class model on the training data.

Methods inherited from class oracle.dmt.jdm.base.OraAlgorithmSettings

getMiningAlgorithm, verify

Methods inherited from class oracle.dmt.jdm.OraDMObject

createException, createException, createRuntimeException, createRuntimeException, getLocalizedMessage, isConnectionOpen, logInfo, logSevere, logTrace, logTrace, unsupported, unsupported

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface javax.datamining.base.AlgorithmSettings

getMiningAlgorithm, verify

Method Detail

getOutlierRate

public double getOutlierRate()

Returns the outlier rate, either set by the user or the default value. The outlier rate is a parameter the approximate rate of outliers (negative predictions) produced by a one-class model on the training data.

Returns:

double

Throws:

javax.datamining.JDMException

setOutlierRate

public void setOutlierRate(double outlierRate)

Sets the outlier rate that is a parameter the approximate rate of outliers (negative predictions) produced by a one-class model on the training data. The value must be between 0 and 1.

Parameters:

outlierRate - The outlier rate to be used.

Returns:

void

Throws:

javax.datamining.JDMException

getActiveLearning

public boolean getActiveLearning()

Returns true if active learning by algorithm is enabled, either set by the user or the default value. Active learning is enabled by default.

Returns:

boolean

Throws:

javax.datamining.JDMException

setActiveLearning

public void setActiveLearning(boolean enable)

Sets to true to enable active learning by algorithm, otherwise sets to false.

Parameters:

enable - True if active learning is enabled.

Returns:

void

Throws:

javax.datamining.JDMException