diff --git a/11-SVM/07-RBF-Kernel/07-RBF-Kernel.ipynb b/11-SVM/07-RBF-Kernel/07-RBF-Kernel.ipynb
deleted file mode 100644
index df2e6da..0000000
--- a/11-SVM/07-RBF-Kernel/07-RBF-Kernel.ipynb
+++ /dev/null
@@ -1,332 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## RBF 核"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Mrh5/h+pnaloM3T7o7uu0adatq+fc3zih56NsK7VrC3z6jXDN8sbvXVuqLtHEmZrA4L5O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\nWQsHBjMza/H/AUTAjc/C5ZbKAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1051cbef0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn import datasets\n",
-    "\n",
-    "X, y = datasets.make_moons(noise=0.15, random_state=666)\n",
-    "\n",
-    "plt.scatter(X[y==0,0], X[y==0,1])\n",
-    "plt.scatter(X[y==1,0], X[y==1,1])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
-       "  decision_function_shape=None, degree=3, gamma=1, kernel='rbf',\n",
-       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-       "  tol=0.001, verbose=False)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "svc = SVC(kernel=\"rbf\", gamma=1)\n",
-    "svc.fit(X, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plot_decision_boundary(model, axis):\n",
-    "    \n",
-    "    x0, x1 = np.meshgrid(\n",
-    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
-    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
-    "    )\n",
-    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
-    "\n",
-    "    y_predict = model.predict(X_new)\n",
-    "    zz = y_predict.reshape(x0.shape)\n",
-    "\n",
-    "    from matplotlib.colors import ListedColormap\n",
-    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
-    "    \n",
-    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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oIvfn0MZHRGRERHzX7Fh0LDu104ZjeaWIPCsiL3uf87t9tsn9eIZsZ/TjqapW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\nAYGIiDz/DwyK68Ja2w2QAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x111bab940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
-    "plt.scatter(X[y==0,0], X[y==0,1])\n",
-    "plt.scatter(X[y==1,0], X[y==1,1])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
-       "  decision_function_shape=None, degree=3, gamma=10, kernel='rbf',\n",
-       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-       "  tol=0.001, verbose=False)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svc_gamma10 = SVC(kernel=\"rbf\", gamma=10)\n",
-    "svc_gamma10.fit(X, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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5iAGByBJmEJFrOGREREQAGBCIiCjCgEBERAAYEIiIKMKAQEREABgQiIgowoBA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\niIgIAAMCERFFGBCIiAgAAwIREUX+H4lKW7cnSsFIAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110fc0f28>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_decision_boundary(svc_gamma10, axis=[-1.5, 2.5, -1.0, 1.5])\n",
-    "plt.scatter(X[y==0,0], X[y==0,1])\n",
-    "plt.scatter(X[y==1,0], X[y==1,1])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
-       "  decision_function_shape=None, degree=3, gamma=100, kernel='rbf',\n",
-       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-       "  tol=0.001, verbose=False)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svc_gamma100 = SVC(kernel=\"rbf\", gamma=100)\n",
-    "svc_gamma100.fit(X, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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RJTFPwHcVewhENFTWXEFbFruROfYQiKhQ3mK9F9/54VYsdiNzDAhEVChvruA9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\nAwIRESX+Hwb8X1Kps0BEAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x111c47898>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_decision_boundary(svc_gamma100, axis=[-1.5, 2.5, -1.0, 1.5])\n",
-    "plt.scatter(X[y==0,0], X[y==0,1])\n",
-    "plt.scatter(X[y==1,0], X[y==1,1])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
-       "  decision_function_shape=None, degree=3, gamma=0.5, kernel='rbf',\n",
-       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-       "  tol=0.001, verbose=False)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svc_gamma05 = SVC(kernel=\"rbf\", gamma=0.5)\n",
-    "svc_gamma05.fit(X, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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mIiIADAhERBRwJiAkKIPxWxF5VUReypJelVZZynWIyGIReUJEfhP8e0bEdoUc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\nEQFgQCAiogADAhERAWBAICKiwP8DTrY1J7kI4XQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10dd26160>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_decision_boundary(svc_gamma05, axis=[-1.5, 2.5, -1.0, 1.5])\n",
-    "plt.scatter(X[y==0,0], X[y==0,1])\n",
-    "plt.scatter(X[y==1,0], X[y==1,1])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
-       "  decision_function_shape=None, degree=3, gamma=0.1, kernel='rbf',\n",
-       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-       "  tol=0.001, verbose=False)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svc_gamma01 = SVC(kernel=\"rbf\", gamma=0.1)\n",
-    "svc_gamma01.fit(X, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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+M2Z2bVlty9nOG8zsZHL9njazuypo4wNmdtzMUvfsRHQtx7Uzhmt5mZl928ye\nT37P/2PKMZVfz4ztzH893T2KP8D76Wyo+A7woRHH/Qi4KOZ2AjPAy8B7gbcBPwCuLrmd/wO4M/n4\nTuC/x3I9s1wfYCewHzDgI8DfVfB/naWdNwD/u4qfxZ42/CpwLXBoyPuVX8uM7YzhWi4A1yYfzwMv\nRfqzmaWdua9nNCMEd3/B3V+suh3jZGznarkOd/8F0C3XUaabgAeTjx8E/m3J5x8ly/W5CXjIO74L\nbDCzhQjbWTl3fxx4fcQhMVzLLO2snLsvuvtTycdLdDIjNw8cVvn1zNjO3KIJCDk4cMDMnkzKXMQo\nrVzH1P9ZOV3q7ovJxz8BLh1yXBXXM8v1ieEaZm3DR5Opg/1m9ivlNC2XGK5lVtFcSzPbCmwD/m7g\nraiu54h2Qs7rWUYto1WBymB8zN2PmdklwGNm9vdJzyOYsst1TGpUO3s/cXc3s2H5xYVfz4Z7Crjc\n3X9uZjuBrwNXVNymuormWprZPwG+CnzG3U9V0YYsxrQz9/UsNSD49GUwcPdjyd/HzexrdIb1QW9g\nAdpZSrmOUe00s5+a2YK7LybD2eNDvkfh1zNFlusTQ8mTsW3o/SV0931m9idmdpG7x1QALYZrOVYs\n19LM1tK5yf5Pd/+rlEOiuJ7j2jnJ9azVlJGZrTez+e7HwG/QeSZDbGIo1/EIcGvy8a3AeSObCq9n\nluvzCPC7SUbHR4CTPVNgZRnbTjPbZNap/25m2+n8Tr1WcjvHieFajhXDtUzO/+fAC+5+95DDKr+e\nWdo50fUse3V8xKr5b9GZi/tH4KfAo8nr7wL2JR+/l06mxw+A5+hM4UTXTn8rE+ElOlkqVbTzl4C/\nAX4IHAA2xnQ9064P8CngU8nHRufBSi8DzzIi86zidt6eXLsfAN8FPlpBG78ELAJnkp/NT0Z6Lce1\nM4Zr+TE662rPAE8nf3bGdj0ztjP39VTpChERAWo2ZSQiIsVRQBAREUABQUREEgoIIiICKCCIiEhC\nAUFERAAFBBERSfx/DWOBc+XXr/cAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d7d97f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_decision_boundary(svc_gamma01, axis=[-1.5, 2.5, -1.0, 1.5])\n",
-    "plt.scatter(X[y==0,0], X[y==0,1])\n",
-    "plt.scatter(X[y==1,0], X[y==1,1])\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/11-SVM/07-What-is-RBF-Kernel/07-What-is-RBF-Kernel.ipynb b/11-SVM/07-What-is-RBF-Kernel/07-What-is-RBF-Kernel.ipynb
new file mode 100644
index 0000000..492cdc1
--- /dev/null
+++ b/11-SVM/07-What-is-RBF-Kernel/07-What-is-RBF-Kernel.ipynb
@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 直观理解高斯核函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = np.arange(-4, 5, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-4, -3, -2, -1,  0,  1,  2,  3,  4])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y = np.array((x >= -2) & (x <= 2), dtype='int')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 1, 1, 1, 1, 1, 0, 0])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAD8CAYAAABkbJM/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ee24438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x[y==0], [0]*len(x[y==0]))\n",
+    "plt.scatter(x[y==1], [0]*len(x[y==1]))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def gaussian(x, l):\n",
+    "    gamma = 1.0\n",
+    "    return np.exp(-gamma * (x-l)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "l1, l2 = -1, 1\n",
+    "\n",
+    "X_new = np.empty((len(x), 2))\n",
+    "for i, data in enumerate(x):\n",
+    "    X_new[i, 0] = gaussian(data, l1)\n",
+    "    X_new[i, 1] = gaussian(data, l2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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r7s376fze2mnVz/ML8BngO8CT4xxuRPo588eA26vqcYCqmuZz93PeAt7Q/c1ur6cT9yPj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\n6P8B0TWCEyVMrKwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f150ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_new[y==0,0], X_new[y==0,1])\n",
+    "plt.scatter(X_new[y==1,0], X_new[y==1,1])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/11-SVM/08-RBF-Kernel-in-scikit-learn/08-RBF-Kernel-in-scikit-learn.ipynb b/11-SVM/08-RBF-Kernel-in-scikit-learn/08-RBF-Kernel-in-scikit-learn.ipynb
new file mode 100644
index 0000000..237ee9b
--- /dev/null
+++ b/11-SVM/08-RBF-Kernel-in-scikit-learn/08-RBF-Kernel-in-scikit-learn.ipynb
@@ -0,0 +1,347 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## scikit-learn 中的 RBF 核"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Mrh5/h+pnaloM3T7o7uu0adatq+fc3zih56NsK7VrC3z6jXDN8sbvXVuqLtHEmZrA4L5O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\nWQsHBjMza/H/AUTAjc/C5ZbKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x102f84908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "X, y = datasets.make_moons(noise=0.15, random_state=666)\n",
+    "\n",
+    "plt.scatter(X[y==0,0], X[y==0,1])\n",
+    "plt.scatter(X[y==1,0], X[y==1,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "def RBFKernelSVC(gamma):\n",
+    "    return Pipeline([\n",
+    "        (\"std_scaler\", StandardScaler()),\n",
+    "        (\"svc\", SVC(kernel=\"rbf\", gamma=gamma))\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma=1, kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False))])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc = RBFKernelSVC(gamma=1)\n",
+    "svc.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_decision_boundary(model, axis):\n",
+    "    \n",
+    "    x0, x1 = np.meshgrid(\n",
+    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
+    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
+    "    )\n",
+    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
+    "\n",
+    "    y_predict = model.predict(X_new)\n",
+    "    zz = y_predict.reshape(x0.shape)\n",
+    "\n",
+    "    from matplotlib.colors import ListedColormap\n",
+    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
+    "    \n",
+    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z46RXjaoq5TrMbIWZPWZmP03+/mDK60o5n4POjy26N/n+82Z2VVFtG7Kd15jZ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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10928ecf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
+    "plt.scatter(X[y==0,0], X[y==0,1])\n",
+    "plt.scatter(X[y==1,0], X[y==1,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma=100, kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False))])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc_gamma100 = RBFKernelSVC(gamma=100)\n",
+    "svc_gamma100.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0uYfahg1nvBdys1ynjB4FMBV9PAXg74afICIXi8h4/DGATwJ43vG4RJVgD7VZ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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x103cc70f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_decision_boundary(svc_gamma100, axis=[-1.5, 2.5, -1.0, 1.5])\n",
+    "plt.scatter(X[y==0,0], X[y==0,1])\n",
+    "plt.scatter(X[y==1,0], X[y==1,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma=10, kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False))])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc_gamma10 = RBFKernelSVC(gamma=10)\n",
+    "svc_gamma10.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KQy8tjf35pLEGF0bGRlOPNYUwEYvmcxEQfgZghYh8GI1AcBOAv2g9QEQuAPC6qqqIrEOj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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f645a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_decision_boundary(svc_gamma10, axis=[-1.5, 2.5, -1.0, 1.5])\n",
+    "plt.scatter(X[y==0,0], X[y==0,1])\n",
+    "plt.scatter(X[y==1,0], X[y==1,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma=0.5, kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False))])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc_gamma05 = RBFKernelSVC(gamma=0.5)\n",
+    "svc_gamma05.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1K3eie3rCqXbloZJrCAAgLR+G1u/jcm3DWpWDwdKRHbj16eX48hMLcOvTy7F0ZEeuxyvb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\niAIMCEREBIABgYiIAv8Px8ZS71udVJIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x103ac9c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_decision_boundary(svc_gamma05, axis=[-1.5, 2.5, -1.0, 1.5])\n",
+    "plt.scatter(X[y==0,0], X[y==0,1])\n",
+    "plt.scatter(X[y==1,0], X[y==1,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma=0.1, kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False))])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc_gamma01 = RBFKernelSVC(gamma=0.1)\n",
+    "svc_gamma01.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10928efd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_decision_boundary(svc_gamma01, axis=[-1.5, 2.5, -1.0, 1.5])\n",
+    "plt.scatter(X[y==0,0], X[y==0,1])\n",
+    "plt.scatter(X[y==1,0], X[y==1,1])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/11-SVM/09-SVM-Regressor/09-SVM-Regressor.ipynb b/11-SVM/09-SVM-Regressor/09-SVM-Regressor.ipynb
new file mode 100644
index 0000000..8cb4b0a
--- /dev/null
+++ b/11-SVM/09-SVM-Regressor/09-SVM-Regressor.ipynb
@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SVM 思想解决回归问题"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "boston = datasets.load_boston()\n",
+    "X = boston.data\n",
+    "y = boston.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import LinearSVR\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "\n",
+    "def StandardLinearSVR(epsilon=0.1):\n",
+    "    return Pipeline([\n",
+    "        ('std_scaler', StandardScaler()),\n",
+    "        ('linearSVR', LinearSVR(epsilon=epsilon))\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linearSVR', LinearSVR(C=1.0, dual=True, epsilon=0.1, fit_intercept=True,\n",
+       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
+       "     random_state=None, tol=0.0001, verbose=0))])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svr = StandardLinearSVR()\n",
+    "svr.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.63618523213237332"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svr.score(X_test, y_test)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/11-SVM/Chapter-11.key b/11-SVM/Chapter-11.key
index faa1448..f8c7835 100644
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