--- /dev/null
+
+///////////////////////////////////////////////////////////////////////////
+// This program is free software: you can redistribute it and/or modify //
+// it under the terms of the version 3 of the GNU General Public License //
+// as published by the Free Software Foundation. //
+// //
+// 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 //
+// General Public License for more details. //
+// //
+// You should have received a copy of the GNU General Public License //
+// along with this program. If not, see <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret, (C) IDIAP //
+// Contact <francois.fleuret@idiap.ch> for comments & bug reports //
+///////////////////////////////////////////////////////////////////////////
+
+/*
+
+ This class is an implementation of the Classifier with a boosting of
+ tree. It works with samples from R^n and has no concept of the
+ pi-features.
+
+*/
+
+#ifndef BOOSTED_CLASSIFIER_H
+#define BOOSTED_CLASSIFIER_H
+
+#include "classifier.h"
+#include "sample_set.h"
+#include "decision_tree.h"
+#include "loss_machine.h"
+
+class BoostedClassifier : public Classifier {
+public:
+
+ int _loss_type;
+ int _nb_weak_learners;
+ DecisionTree **_weak_learners;
+
+public:
+
+ BoostedClassifier(int nb_weak_learners);
+ BoostedClassifier();
+ virtual ~BoostedClassifier();
+
+ virtual scalar_t response(SampleSet *sample_set, int n_sample);
+ virtual void train(LossMachine *loss_machine, SampleSet *train, scalar_t *response);
+
+ virtual void tag_used_features(bool *used);
+ virtual void re_index_features(int *new_indexes);
+
+ virtual void read(istream *is);
+ virtual void write(ostream *os);
+};
+
+#endif