diff --git a/sheet11/sheet11solutions.ipynb b/sheet11/sheet11solutions.ipynb
index cb9e520..be132dc 100644
--- a/sheet11/sheet11solutions.ipynb
+++ b/sheet11/sheet11solutions.ipynb
@@ -517,12 +517,12 @@
     "\n",
     "| Classifier           | Biases and Assumptions | Separatrices | Sensitivity | Locality | Parameters | Speed |\n",
     "|----------------------|------------------------|--------------|-------------|----------|------------|-------|\n",
-    "| Euclidean classifier | ? | linear | sensitive to far outliers | global | none | very fast |\n",
+    "| Euclidean classifier | voronoi tesselation around class centers | linear | sensitive to far outliers | global | none | very fast |\n",
     "| Linear discriminant analysis | normally distributed data with equal covariances | linear | sensitive to far outliers | global | none | very fast |\n",
     "| Quadratic classifier (e.g. QDA) | ? | conic: e.g. hyperbola, parabola, ellipsis, line | sensitive to outliers | global | none | fast |\n",
     "| Polynom classifier | ? | almost arbitrary | overfitting for high degrees | global | polynomial degree | fast |\n",
-    "| Nearest neighbor classifier  | ? | implicit: neighbors (voronoi cells around training data) | distance function | local | number of neighbors $k$ | $\\mathcal{O}(N)$ (instant training, linear classification) |\n",
-    "| Bayesian classifier | ? | discriminate functions (probability distributions) | overlapping classifications (only probabilities), noise is modeled | global | none | varies (underlying data and method for discriminate functions, see ML-09 Slides 5f) |\n",
+    "| Nearest neighbor classifier  | classification for neighbors are similar | implicit: neighbors (voronoi cells around training data) | distance function | local | number of neighbors $k$ | $\\mathcal{O}(N)$ (instant training, linear classification) |\n",
+    "| Bayesian classifier | expected cost is minimized | discriminate functions (probability distributions) | overlapping classifications (only probabilities), noise is modeled | global | none | varies (underlying data and method for discriminate functions, see ML-09 Slides 5f) |\n",
     "| MLP (not necessarily binary) | smooth interpolation | almost arbitrary | noise sensitive | global | activation functions, learning rate | slow |\n",
     "| RBFN (not necessarily binary) | locality in data/clusters | ellipses/circular | robust to noise | local | regions of responsibility,  learning rate | comparably slow |\n",
     "| SVM | mercer's condition, input mapping, kernel function | high dimensional hyperplane, nonlinear in data space | handles noise with slacking variables | global | none | efficient |"