diff --git a/openbr/plugins/liblinear.cpp b/openbr/plugins/liblinear.cpp
index 4ecf72c..7094b9a 100644
--- a/openbr/plugins/liblinear.cpp
+++ b/openbr/plugins/liblinear.cpp
@@ -16,12 +16,9 @@ namespace br
 class LinearSVM : public Transform
 {
     Q_OBJECT
-    Q_ENUMS(Kernel)
-    Q_ENUMS(Type)
-    Q_PROPERTY(Kernel kernel READ get_kernel WRITE set_kernel RESET reset_kernel STORED false)
-    Q_PROPERTY(Type type READ get_type WRITE set_type RESET reset_type STORED false)
+    Q_ENUMS(Solver)
+    Q_PROPERTY(Solver solver READ get_solver WRITE set_solver RESET reset_solver STORED false)
     Q_PROPERTY(float C READ get_C WRITE set_C RESET reset_C STORED false)
-    Q_PROPERTY(float gamma READ get_gamma WRITE set_gamma RESET reset_gamma STORED false)
     Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
     Q_PROPERTY(QString outputVariable READ get_outputVariable WRITE set_outputVariable RESET reset_outputVariable STORED false)
     Q_PROPERTY(bool returnDFVal READ get_returnDFVal WRITE set_returnDFVal RESET reset_returnDFVal STORED false)
@@ -30,22 +27,21 @@ class LinearSVM : public Transform
     Q_PROPERTY(bool balanceFolds READ get_balanceFolds WRITE set_balanceFolds RESET reset_balanceFolds STORED false)
 
 public:
-    enum Kernel { Linear = CvSVM::LINEAR,
-                  Poly = CvSVM::POLY,
-                  RBF = CvSVM::RBF,
-                  Sigmoid = CvSVM::SIGMOID };
-
-    enum Type { C_SVC = CvSVM::C_SVC,
-                NU_SVC = CvSVM::NU_SVC,
-                ONE_CLASS = CvSVM::ONE_CLASS,
-                EPS_SVR = CvSVM::EPS_SVR,
-                NU_SVR = CvSVM::NU_SVR};
+    enum Solver { L2R_LR,
+                  L2R_L2LOSS_SVC_DUAL,
+                  L2R_L2LOSS_SVC,
+                  L2R_L1LOSS_SVC_DUAL,
+                  MCSVM_CS,
+                  L1R_L2LOSS_SVC,
+                  L1R_LR,
+                  L2R_LR_DUAL,
+                  L2R_L2LOSS_SVR,
+                  L2R_L2LOSS_SVR_DUAL,
+                  L2R_L1LOSS_SVR_DUAL };
 
 private:
-    BR_PROPERTY(Kernel, kernel, Linear)
-    BR_PROPERTY(Type, type, C_SVC)
-    BR_PROPERTY(float, C, -1)
-    BR_PROPERTY(float, gamma, -1)
+    BR_PROPERTY(Solver, solver, L2R_L2LOSS_SVC_DUAL)
+    BR_PROPERTY(float, C, 1)
     BR_PROPERTY(QString, inputVariable, "Label")
     BR_PROPERTY(QString, outputVariable, "")
     BR_PROPERTY(bool, returnDFVal, false)
@@ -88,9 +84,12 @@ private:
         }
 
         parameter param;
-        param.C = 1;
+        // TODO: Support grid search
+        param.C = C;
         param.eps = FLT_EPSILON;
-        param.solver_type = L2R_L2LOSS_SVC_DUAL;
+        param.solver_type = solver;
+
+        // TODO: Support weights
         param.nr_weight = 0;
         param.p = 1;
         param.weight_label = NULL;
@@ -98,9 +97,9 @@ private:
 
         m = train_svm(&prob, &param);
 
-        delete x_space;
-        delete prob.x;
-        delete prob.y;
+        delete[] prob.y;
+        delete[] prob.x;
+        delete[] x_space;
     }
 
     void project(const Template &src, Template &dst) const
@@ -108,7 +107,7 @@ private:
         dst = src;
 
         Mat sample = src.m().reshape(1,1);
-        feature_node *x_space = new feature_node[sample.cols];
+        feature_node *x_space = new feature_node[sample.cols+1];
 
         // Assign the address of the ith instance to be the address of the jth feature
         for (int j=0; j<sample.cols; j++) {
@@ -117,12 +116,14 @@ private:
         }
         x_space[sample.cols].index = -1;
 
+        // TODO: Call appropriate function based on solver
         double prob_estimates[1];
         float prediction = predict_values(m,x_space,prob_estimates);
 
-        delete x_space;
         dst.m() = Mat(1, 1, CV_32F);
         dst.m().at<float>(0, 0) = prob_estimates[0];
+
+        delete[] x_space;
     }
 
     void store(QDataStream &stream) const