Added missing citations

Signed-off-by: Jim Martens <github@2martens.de>

ma.bib

@@ -849,4 +849,64 @@ to construct explicit models for non-normal classes. Application includes infere
   timestamp = {2019.08.27},
 }
 
+@InProceedings{Hautamaki2004,
+  author    = {V. Hautamaki and I. Karkkainen and P. Franti},
+  title     = {Outlier detection using k-nearest neighbour graph},
+  booktitle = {{ICPR} 2004},
+  year      = {2004},
+  publisher = {{IEEE}},
+  doi       = {10.1109/icpr.2004.1334558},
+  abstract  = {We present an outlier detection using indegree number (ODIN) algorithm that utilizes k-nearest neighbour graph. Improvements to existing kNN distance-based method are also proposed. We compare the methods with real and synthetic datasets. The results show that the proposed method achieves reasonable results with synthetic data and outperforms compared methods with real data sets with small number of observations.},
+  file      = {:/home/jim/Documents/Studium/MA/Literatur/58_k-nearest-neighbour.pdf:PDF},
+  owner     = {jim},
+  timestamp = {2019.09.09},
+}
+
+@Article{Jordan1994,
+  author    = {Michael I. Jordan and Robert A. Jacobs},
+  title     = {Hierarchical Mixtures of Experts and the {EM} Algorithm},
+  journal   = {Neural Computation},
+  year      = {1994},
+  volume    = {6},
+  number    = {2},
+  pages     = {181--214},
+  doi       = {10.1162/neco.1994.6.2.181},
+  file      = {:/home/jim/Documents/Studium/MA/Literatur/59_k-means.pdf:PDF},
+  owner     = {jim},
+  publisher = {{MIT} Press - Journals},
+  timestamp = {2019.09.09},
+}
+
+@Article{Song2002,
+  author    = {Qing Song and Wenjie Hu and Wenfang Xie},
+  title     = {Robust support vector machine with bullet hole image classification},
+  journal   = {{IEEE} Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)},
+  year      = {2002},
+  volume    = {32},
+  number    = {4},
+  pages     = {440--448},
+  doi       = {10.1109/tsmcc.2002.807277},
+  file      = {:/home/jim/Documents/Studium/MA/Literatur/60_robust-support-vector-machine.pdf:PDF},
+  owner     = {jim},
+  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
+  timestamp = {2019.09.09},
+}
+
+@Article{Filippone2011,
+  author    = {Maurizio Filippone and Guido Sanguinetti},
+  title     = {A Perturbative Approach to Novelty Detection in Autoregressive Models},
+  journal   = {{IEEE} Transactions on Signal Processing},
+  year      = {2011},
+  volume    = {59},
+  number    = {3},
+  month     = {mar},
+  pages     = {1027--1036},
+  doi       = {10.1109/tsp.2010.2094609},
+  abstract  = {We propose a new method to perform novelty detection in dynamical systems governed by linear autoregressive models. The method is based on a perturbative expansion to a statistical test whose leading term is the classical F-test, and whose O(1/n) correction can be approximated as a function of the number of training points and the model order alone. The method can be justified as an approximation to an information theoretic test. We demonstrate on several synthetic examples that the first correction to the F-test can dramatically improve the control over the false positive rate of the system. We also test the approach on some real time series data, demonstrating that the method still retains a good accuracy in detecting novelties.},
+  file      = {:/home/jim/Documents/Studium/MA/Literatur/61_perturbative-approach-to-novelty-detection-autoregressive-models.pdf:PDF},
+  owner     = {jim},
+  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
+  timestamp = {2019.09.09},
+}
+
 @Comment{jabref-meta: databaseType:biblatex;}