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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/svm/LibSVMOneClassOutlierDetection.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/svm/LibSVMOneClassOutlierDetection.java
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+package de.lmu.ifi.dbs.elki.algorithm.outlier.svm;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2014
+ Ludwig-Maximilians-Universität München
+ Lehr- und Forschungseinheit für Datenbanksysteme
+ ELKI Development Team
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+import libsvm.svm;
+import libsvm.svm_model;
+import libsvm.svm_node;
+import libsvm.svm_parameter;
+import libsvm.svm_print_interface;
+import libsvm.svm_problem;
+import de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm;
+import de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
+import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
+import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
+import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.relation.DoubleRelation;
+import de.lmu.ifi.dbs.elki.database.relation.MaterializedDoubleRelation;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
+import de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.EnumParameter;
+
+/**
+ * Outlier-detection using one-class support vector machines.
+ * 
+ * Important note: from literature, the one-class SVM is trained as if 0 was the
+ * only counterexample. Outliers will only be detected when they are close to
+ * the origin!
+ * 
+ * <p>
+ * Reference:<br />
+ * B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson<br />
+ * Estimating the support of a high-dimensional distribution<br />
+ * Neural computation 13.7
+ * </p>
+ * 
+ * @author Erich Schubert
+ * 
+ * @param V vector type
+ */
+@Reference(authors = "B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson", //
+title = "Estimating the support of a high-dimensional distribution", //
+booktitle = "Neural computation 13.7")
+public class LibSVMOneClassOutlierDetection<V extends NumberVector> extends AbstractAlgorithm<OutlierResult> implements OutlierAlgorithm {
+  /**
+   * Class logger.
+   */
+  private static final Logging LOG = Logging.getLogger(LibSVMOneClassOutlierDetection.class);
+
+  /**
+   * Kernel functions. Expose as enum for convenience.
+   * 
+   * @apiviz.exclude
+   */
+  public static enum SVMKernel { //
+    LINEAR, // Linear
+    QUADRATIC, // Quadratic
+    CUBIC, // Cubic
+    RBF, // Radial basis functions
+    SIGMOID, // Sigmoid
+  }
+
+  /**
+   * Kernel function in use.
+   */
+  protected SVMKernel kernel = SVMKernel.RBF;
+
+  /**
+   * Constructor.
+   * 
+   * @param kernel Kernel to use with SVM.
+   */
+  public LibSVMOneClassOutlierDetection(SVMKernel kernel) {
+    super();
+    this.kernel = kernel;
+  }
+
+  /**
+   * Run one-class SVM.
+   * 
+   * @param relation Data relation
+   * @return Outlier result.
+   */
+  public OutlierResult run(Relation<V> relation) {
+    final int dim = RelationUtil.dimensionality(relation);
+    final ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
+
+    svm.svm_set_print_string_function(LOG_HELPER);
+
+    svm_parameter param = new svm_parameter();
+    param.svm_type = svm_parameter.ONE_CLASS;
+    param.kernel_type = svm_parameter.LINEAR;
+    param.degree = 3;
+    switch(kernel){
+    case LINEAR:
+      param.kernel_type = svm_parameter.LINEAR;
+      break;
+    case QUADRATIC:
+      param.kernel_type = svm_parameter.POLY;
+      param.degree = 2;
+      break;
+    case CUBIC:
+      param.kernel_type = svm_parameter.POLY;
+      param.degree = 3;
+      break;
+    case RBF:
+      param.kernel_type = svm_parameter.RBF;
+      break;
+    case SIGMOID:
+      param.kernel_type = svm_parameter.SIGMOID;
+      break;
+    default:
+      throw new AbortException("Invalid kernel parameter: " + kernel);
+    }
+    // TODO: expose additional parameters to the end user!
+    param.nu = 0.05;
+    param.coef0 = 0.;
+    param.cache_size = 100;
+    param.C = 1e2;
+    param.eps = 1e-4; // not used by one-class?
+    param.p = 0.1; // not used by one-class?
+    param.shrinking = 0;
+    param.probability = 0;
+    param.nr_weight = 0;
+    param.weight_label = new int[0];
+    param.weight = new double[0];
+    param.gamma = 1e-4 / dim;
+
+    // Transform data:
+    svm_problem prob = new svm_problem();
+    prob.l = relation.size();
+    prob.x = new svm_node[prob.l][];
+    prob.y = new double[prob.l];
+    {
+      DBIDIter iter = ids.iter();
+      for(int i = 0; i < prob.l && iter.valid(); iter.advance(), i++) {
+        V vec = relation.get(iter);
+        // TODO: support compact sparse vectors, too!
+        svm_node[] x = new svm_node[dim];
+        for(int d = 0; d < dim; d++) {
+          x[d] = new svm_node();
+          x[d].index = d + 1;
+          x[d].value = vec.doubleValue(d);
+        }
+        prob.x[i] = x;
+        prob.y[i] = +1;
+      }
+    }
+
+    if(LOG.isVerbose()) {
+      LOG.verbose("Training one-class SVM...");
+    }
+    String err = svm.svm_check_parameter(prob, param);
+    if(err != null) {
+      LOG.warning("svm_check_parameter: " + err);
+    }
+    svm_model model = svm.svm_train(prob, param);
+
+    if(LOG.isVerbose()) {
+      LOG.verbose("Predicting...");
+    }
+    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_DB);
+    DoubleMinMax mm = new DoubleMinMax();
+    {
+      DBIDIter iter = ids.iter();
+      double[] buf = new double[svm.svm_get_nr_class(model)];
+      for(int i = 0; i < prob.l && iter.valid(); iter.advance(), i++) {
+        V vec = relation.get(iter);
+        svm_node[] x = new svm_node[dim];
+        for(int d = 0; d < dim; d++) {
+          x[d] = new svm_node();
+          x[d].index = d + 1;
+          x[d].value = vec.doubleValue(d);
+        }
+        svm.svm_predict_values(model, x, buf);
+        double score = -buf[0] / param.gamma; // Heuristic rescaling, sorry.
+        // Unfortunately, libsvm one-class currently yields a binary decision.
+        scores.putDouble(iter, score);
+        mm.put(score);
+      }
+    }
+    DoubleRelation scoreResult = new MaterializedDoubleRelation("One-Class SVM Decision", "svm-outlier", scores, ids);
+    OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(mm.getMin(), mm.getMax(), Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.);
+    return new OutlierResult(scoreMeta, scoreResult);
+  }
+
+  @Override
+  public TypeInformation[] getInputTypeRestriction() {
+    return TypeUtil.array(TypeUtil.NUMBER_VECTOR_FIELD);
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
+  /**
+   * Setup logging helper for SVM.
+   */
+  static final svm_print_interface LOG_HELPER = new svm_print_interface() {
+    @Override
+    public void print(String arg0) {
+      if(LOG.isVerbose()) {
+        LOG.verbose(arg0);
+      }
+    }
+  };
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   * 
+   * @param <V> Vector type
+   */
+  public static class Parameterizer<V extends NumberVector> extends AbstractParameterizer {
+    /**
+     * Parameter for kernel function.
+     */
+    private static final OptionID KERNEL_ID = new OptionID("svm.kernel", "Kernel to use with SVM.");
+
+    /**
+     * Kernel in use.
+     */
+    protected SVMKernel kernel = SVMKernel.RBF;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+
+      EnumParameter<SVMKernel> kernelP = new EnumParameter<>(KERNEL_ID, SVMKernel.class, SVMKernel.RBF);
+      if(config.grab(kernelP)) {
+        kernel = kernelP.getValue();
+      }
+    }
+
+    @Override
+    protected LibSVMOneClassOutlierDetection<V> makeInstance() {
+      return new LibSVMOneClassOutlierDetection<>(kernel);
+    }
+  }
+}