diff --git a/scikits/learn/svm/liblinear.py b/scikits/learn/svm/liblinear.py
index 17cac4062f2a3c243ad16d873f14cbbbb465abd3..3a0f58691558511ce9865d335752b94eea83702e 100644
--- a/scikits/learn/svm/liblinear.py
+++ b/scikits/learn/svm/liblinear.py
@@ -37,19 +37,18 @@ class LinearSVC(BaseLibLinear, ClassifierMixin):
 
     Attributes
     ----------
-    `support_` : array-like, shape = [nSV, n_features]
-        Support vectors.
-
-    `dual_coef_` : array, shape = [n_class-1, nSV]
-        Coefficients of the support vector in the decision function.
-
-    `coef_` : array, shape = [n_class-1, n_features]
+    `coef_` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
         Weights asigned to the features (coefficients in the primal
         problem). This is only available in the case of linear kernel.
 
-    `intercept_` : array, shape = [n_class-1]
+    `intercept_` : array, shape = [1] if n_classes == 2 else [n_classes]
         Constants in decision function.
 
+    References
+    ----------
+    LIBLINEAR -- A Library for Large Linear Classification
+    http://www.csie.ntu.edu.tw/~cjlin/liblinear/
+
     """
 
     # all the implementation is provided by the mixins
diff --git a/scikits/learn/svm/sparse/liblinear.py b/scikits/learn/svm/sparse/liblinear.py
index fd1c4d92707d4e1e00af3865e796a96bec9aab4e..57557b7f24d5f6fd04090b01fe1b4067aaf85ec7 100644
--- a/scikits/learn/svm/sparse/liblinear.py
+++ b/scikits/learn/svm/sparse/liblinear.py
@@ -35,19 +35,11 @@ class LinearSVC(SparseBaseLibLinear, ClassifierMixin):
 
     Attributes
     ----------
-    `support_` : array-like, shape = [nSV, n_features]
-        Support vectors
-
-    `dual_coef_` : array, shape = [n_classes-1, nSV]
-        Coefficient of the support vector in the decision function,
-        where n_classes is the number of classes and nSV is the number
-        of support vectors.
-
-    `coef_` : array, shape = [n_classes-1, n_features]
+    `coef_` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
         Wiehgiths asigned to the features (coefficients in the primal
         problem). This is only available in the case of linear kernel.
 
-    `intercept_` : array, shape = [n_classes-1]
+    `intercept_` : array, shape = [1] if n_classes == 2 else [n_classes]
         constants in decision function
 
     References