Use LinearSVC's docstring instead of outdated one.

sklearn/svm/sparse/classes.py

@@ -183,60 +183,12 @@ class LinearSVC(SparseBaseLibLinear, ClassifierMixin,
     choice of penalties and loss functions and should be faster for
     huge datasets.
 
-    Parameters
-    ----------
-    loss : string, 'l1' or 'l2' (default 'l2')
-        Specifies the loss function. With 'l1' it is the standard SVM
-        loss (a.k.a. hinge Loss) while with 'l2' it is the squared loss.
-        (a.k.a. squared hinge Loss)
-
-    penalty : string, 'l1' or 'l2' (default 'l2')
-        Specifies the norm used in the penalization. The 'l2' penalty
-        is the standard used in SVC. The 'l1' leads to ``coef_``
-        vectors that are sparse.
-
-    C : float, optional (default=1.0)
-        penalty parameter C of the error term.
-
-    dual : bool, (default True)
-        Select the algorithm to either solve the dual or primal
-        optimization problem.
-
-    intercept_scaling : float, default: 1
-        when self.fit_intercept is True, instance vector x becomes
-        [x, self.intercept_scaling],
-        i.e. a "synthetic" feature with constant value equals to
-        intercept_scaling is appended to the instance vector.
-        The intercept becomes intercept_scaling * synthetic feature weight
-        Note! the synthetic feature weight is subject to l1/l2 regularization
-        as all other features.
-        To lessen the effect of regularization on synthetic feature weight
-        (and therefore on the intercept) intercept_scaling has to be increased
-
-    Attributes
-    ----------
-    `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 = [1] if n_classes == 2 else [n_classes]
-        constants in decision function
+    See :class:`sklearn.svm.SVC` for a complete list of parameters
 
     Notes
     -----
-    The underlying C implementation uses a random number generator to
-    select features when fitting the model. It is thus not uncommon,
-    to have slightly different results for the same input data. If
-    that happens, try with a smaller eps parameter.
-
-    See also
-    --------
-    SVC
-
-    References
-    ----------
-    LIBLINEAR -- A Library for Large Linear Classification
-    http://www.csie.ntu.edu.tw/~cjlin/liblinear/
-
+    For best results, this accepts a matrix in csr format
+    (scipy.sparse.csr), but should be able to convert from any array-like
+    object (including other sparse representations).
     """
     pass