diff --git a/openbr/plugins/classification/svm.cpp b/openbr/plugins/classification/svm.cpp
index c361957..e6f42d1 100644
--- a/openbr/plugins/classification/svm.cpp
+++ b/openbr/plugins/classification/svm.cpp
@@ -26,12 +26,19 @@ using namespace cv;
 namespace br
 {
 
-static void trainSVM(SVM &svm, Mat data, Mat lab, CvSVMParams params, float C, float gamma, int folds, bool balanceFolds)
+static void trainSVM(SVM &svm, Mat data, Mat lab, int kernel, int type, float C, float gamma, int folds, bool balanceFolds, int termCriteria)
 {
     if (data.type() != CV_32FC1)
         qFatal("Expected single channel floating point training data.");
 
-    if ((C == -1) || ((gamma == -1) && (params.kernel_type == CvSVM::RBF))) {
+    CvSVMParams params;
+    params.kernel_type = kernel;
+    params.svm_type = type;
+    params.p = 0.1;
+    params.nu = 0.5;
+    params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, termCriteria, FLT_EPSILON);
+
+    if ((C == -1) || ((gamma == -1) && (kernel == CvSVM::RBF))) {
         try {
             svm.train_auto(data, lab, Mat(), Mat(), params, folds,
                            CvSVM::get_default_grid(CvSVM::C),
@@ -76,7 +83,6 @@ class SVMTransform : public Transform
     Q_PROPERTY(int termCriteria READ get_termCriteria WRITE set_termCriteria RESET reset_termCriteria STORED false)
     Q_PROPERTY(int folds READ get_folds WRITE set_folds RESET reset_folds STORED false)
     Q_PROPERTY(bool balanceFolds READ get_balanceFolds WRITE set_balanceFolds RESET reset_balanceFolds STORED false)
-    Q_PROPERTY(int degree READ get_degree WRITE set_degree RESET reset_degree STORED false)
 
 public:
     enum Kernel { Linear = CvSVM::LINEAR,
@@ -101,7 +107,6 @@ private:
     BR_PROPERTY(int, termCriteria, 1000)
     BR_PROPERTY(int, folds, 5)
     BR_PROPERTY(bool, balanceFolds, false)
-    BR_PROPERTY(int, degree, 2)
 
     SVM svm;
     QHash<QString, int> labelMap;
@@ -122,16 +127,8 @@ private:
             QList<int> dataLabels = _data.indexProperty(inputVariable, labelMap, reverseLookup);
             lab = OpenCVUtils::toMat(dataLabels);
         }
-        
-        CvSVMParams params;
-        params.kernel_type = kernel;
-        params.svm_type = type;
-        params.p = 0.1;
-        params.nu = 0.5;
-        params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, termCriteria, FLT_EPSILON);
-        params.degree = degree;
-        
-        trainSVM(svm, data, lab, params, C, gamma, folds, balanceFolds);
+
+        trainSVM(svm, data, lab, kernel, type, C, gamma, folds, balanceFolds, termCriteria);
     }
 
     void project(const Template &src, Template &dst) const
@@ -195,7 +192,6 @@ class SVMDistance : public Distance
     Q_PROPERTY(int termCriteria READ get_termCriteria WRITE set_termCriteria RESET reset_termCriteria STORED false)
     Q_PROPERTY(int folds READ get_folds WRITE set_folds RESET reset_folds STORED false)
     Q_PROPERTY(bool balanceFolds READ get_balanceFolds WRITE set_balanceFolds RESET reset_balanceFolds STORED false)
-    Q_PROPERTY(int degree READ get_degree WRITE set_degree RESET reset_degree STORED false)
 
 public:
     enum Kernel { Linear = CvSVM::LINEAR,
@@ -216,7 +212,6 @@ private:
     BR_PROPERTY(int, termCriteria, 1000)
     BR_PROPERTY(int, folds, 5)
     BR_PROPERTY(bool, balanceFolds, false)
-    BR_PROPERTY(int, degree, 2)
 
     SVM svm;
 
@@ -240,16 +235,8 @@ private:
             }
         deltaData = deltaData.rowRange(0, index);
         deltaLab = deltaLab.rowRange(0, index);
-        
-        CvSVMParams params;
-        params.kernel_type = kernel;
-        params.svm_type = type;
-        params.p = 0.1;
-        params.nu = 0.5;
-        params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, termCriteria, FLT_EPSILON);
-        params.degree = degree;
-
-        trainSVM(svm, deltaData, deltaLab, params, -1, -1, folds, balanceFolds);
+
+        trainSVM(svm, deltaData, deltaLab, kernel, type, -1, -1, folds, balanceFolds, termCriteria);
     }
 
     float compare(const Mat &a, const Mat &b) const