Common use of Bibliography Clause in Contracts

Bibliography. Xxxxxx, X. (1994, Jan). Numerics of Gram-Xxxxxxx Orthogonalization. Linear Algebra and its Applications 197-198, 297–316. Xxxxxxxx, X., X. Dorigo, and G. Theraulaz (1999). Swarm Intelligence: from Natural to Artificial Systems. Number 1. Oxford university press. Xxxxxxx, X.-F., X. X. Xxxxxxx, X. Xxxxxxxxxx, and X. X. Xxxxxxxxxxxx (2006). Numerical Optimization: Theoretical and Practical Aspects. Springer Science & Business Media. Xxxx, X. and X. Xxxxxxxxxxxx (2004). Convex Optimization. New York, NY, USA: Cambridge University Press. Xxxxxxx, X. (1996). Bagging Predictors. Machine Learning 24 (2), 123–140. Xxxxxxx, X., X. Friedman, X. X. Xxxxx, and X. X. Xxxxxx (1984). Classification and Regression Trees. CRC press. Xxxxxxxxx, D., X. Xxxxx, X. Xxxxxx, X. X. Xxxxxx, and X. Xxxx (2010). Mirrored Sampling and Sequential Selection for Evolution Strategies. In X. Xxxxxxxx, X. Xxxxx, X. Xxxxxxxxx, and X. Xxxxxxx (Eds.), Proceedings of the 11th inter- national conference on Parallel problem solving from nature: Part I, PPSN’10, Springer-Verlag, Berlin, pp. 11–21. Xxxxxxx, X. X. (2003). Radial Basis Functions: Theory and Implementations, Volume 12. Cambridge university press. Xxxxxxxx, X., X. X. X. Xxxxxxxx, and X. Xxxxxx (2013, February). A Framework for Evaluating Approximation Methods for Gaussian Process Regression. X. Xxxx. Learn. Res. 14 (1), 333–350. Xxxx, X. and X. Xxx (2009, March). Bagging for Gaussian Process Regression. Xxxxxxxxx, X. and X. Xxxxxxxxxxx (2013). Fast Computation of the Multi-points Expected Improvement with Applications in Batch Selection. In X. Xxxxxxx and X. Xxxxxxxx (Eds.), Learning and Intelligent Optimization, Berlin, Heidelberg, pp. 59–69. Springer Berlin Heidelberg. Xxxxxxx, N. (1990). The Origins of Kriging. Mathematical geology 22 (3), 239–252. Xxxxxxx, N. (2015). Statistics for spatial data. Xxxx Xxxxx & Sons.

Bibliography. XxxxxxXxxxxxx, X. (1994, Jan2019). Numerics The democratic aspect of Gram-Xxxxxxx Orthogonalizationmachine learning: Limitations and opportunities for xxxxxxxxx’x disease. Linear Algebra and its Applications 197-198Journal of the Movement Disorder Society 34 (2), 297–316164–166. XxxxxxxxXxxxxxx, X. (2001, Oct). Random forests. Machine Learning 45 (1), 5–32. Xxxxxxx, X. X., X. DorigoX. Xxxx, and G. Theraulaz (1999). Swarm Intelligence: from Natural to Artificial Systems. Number 1. Oxford university press. XxxxxxxX. X. Xxxx, X.-F.X. Xxxx, X. X. Xxxxxxxxx, X. X. Xxxxxxx, X. XxxxxxxxxxX. Xxxxxxx-Xxxxxx, and E. K. St. Xxxxx (2016). Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants. American Epilepsy Society, Chicago. Xxxxxx, X., X. X. Xxxx, and X. X. Xxxxxxxxxxxx xx Xxxxxxx (20062010). Numerical Optimization: Theoretical A tutorial on bayesian opti- mization of expensive cost functions, with application to active user modeling and Practical Aspectshierarchical reinforcement learning. Springer Science & Business MediaCoRR abs/1012.2599. XxxxXxxxxxxxx, X. X. and X. Xxxxxxxxxxxx X. Xxxxx (20041991). Convex OptimizationTime Series: Theory and Methods. New York, NY, USA: Cambridge University PressSpringer. Xxxx, A., X. Xxxxxxx, and X. X. Xxxx (2017). Multi-objective genetic programming for feature extraction and data visualization. Soft Computing 21 (8), 2069–2089. Xxxxxxx, X. (19962018). Bagging PredictorsHow artificial intelligence enables smarter claims process- ing. Machine Learning 24 (2)xxxxx://xxx.xxxxxxxxx.xxx/2017/10/how-artificial-intelligence-enables- smarter-claims-processing/. Retrieved June 28, 123–1402018. Xxxxxxxx, M., X. Xxxxxxx, X.and X. Xxxxxxxxx (2011). Multi objective genetic pro- gramming for feature construction in classification problems. In Learning and Intelligent Optimization, Berlin, Germany, pp. 503–506. Springer. Xxxxxxxxxxxxx, X. Friedmanand X. Xxxxx (2014). A survey on feature selection methods. Xxxxxxxxxx, M., X. X. Xxxxxxxx, X. X. Xxxxx, and X. Xxxx, X. Xxxxxx (1984). Classification and Regression Trees. CRC press. Xxxxxxxxx, D.Xxxxxxxxxxxx, X. Xxxxx, X. Xxxxxx, X. X. XxxxxxXxxx, and X. Xxxx (20102019). Mirrored Sampling Phase lag index and Sequential Selection spectral power as QEEG fea- tures for Evolution Strategiesidentification of patients with mild cognitive impairment in xxxxxxxxx’x disease. Clinical Neurophysiology 130 (10), 1937 – 1944. Xxxxxxx, X. (2018). Deep learning with Python (1st ed.). Xxxxxxx Publications Co. Xxxxxx, M., X. Xxxxx, X. Xxxxxxx, and X. X. Xxxxx-Xxxxx (2018). Time series feature extraction on basis of scalable hypothesis tests (tsfresh – a python package). Neurocomputing 307, 72–77. Xxxxxx, M., X. X. Xxxxx-Xxxxx, and X. Xxxxxx (2016). Distributed and parallel time series feature extraction for industrial big data applications. CoRR abs/1610.07717. Xxxxx, X., X. Xxxxxxxx, X. Cho, and X. Xxxxxx (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs/1412.3555. Xxxxxxx, X. and X. X. Xxxx (2015). Hyperparameter search in machine learning. Xxxxx, X. X., X. Xxxxxxxxxx, X. Xxxx, X. Xxxxx, X. Xxxx, and X. Xxxxxxxxxxxx (2016). Increase of EEG spectral theta power indicates higher risk of the development of severe cognitive decline in xxxxxxxxx’x disease after 3 years. Frontiers in Aging Neuroscience 8, 284. Xxxxxx, X. and X. Xxxxxx (2011). Autoregressive kernels for time series. de Xxxxxxx, X. (Retrieved June 19, 2019). MLBOX. xxxxx://xxxxxx.xxx/AxeldeRomblay/MLBox. xx Xx, X. X. X., X. X. X. X. Xxxxx, L. O. V. B. Xxxxxxxx, and X. X. Xxxxx (2017). RECIPE: A grammar-based framework for automatically evolving classification pipelines. In Genetic Programming, Xxxx, pp. 246–261. Springer. xx Xxxxx, A., X. XxxxxxxxXxxxx, and J. Malotta (2017). Automatische Erkennung und Bewertung von Low-Speed-Cashs. World Patent WO2017009201. Xxx, X. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. New York, NY, USA: Xxxx Xxxxx & Sons, Inc. Xxxxxx, X. (2006). Statistical comparisons of classifiers over multiple data sets. Xxxxxx, X., X. Arnaout, X. X. Xxxxxxxxx, and X. Xxxxxxxxxx (2012). Multivariate time series classification by combining trend-based and value-based approxi- mations. In Computational Science and Its Applications – ICCSA 2012, pp. 392–403. Springer. Xxxxx, H. I., X. Xxxxxxxxx, X. Xxxxx, X. Xxxxxxxxx, and X. Xxxxxxx P.-X. Xxxxxx (Eds.), Proceedings of the 11th inter- national conference on Parallel problem solving from nature: Part I, PPSN’10, Springer-Verlag, Berlin, pp. 11–21. Xxxxxxx, X. X. (20032018). Radial Basis FunctionsDeep learning for time series classification: Theory and Implementations, Volume 12A review. Cambridge university press. Xxxxxxxx, X., X. X. X. Xxxxxxxx, and X. Xxxxxx (2013, February). A Framework for Evaluating Approximation Methods for Gaussian Process Regression. X. Xxxx. Learn. Res. 14 (1), 333–350. Xxxx, X. and X. Xxx (2009, March). Bagging for Gaussian Process Regression. Xxxxxxxxx, X. and X. Xxxxxxxxxxx (2013). Fast Computation of the Multi-points Expected Improvement with Applications in Batch Selection. In X. Xxxxxxx and X. Xxxxxxxx (EdsCoRR abs/1809.04356.), Learning and Intelligent Optimization, Berlin, Heidelberg, pp. 59–69. Springer Berlin Heidelberg. Xxxxxxx, N. (1990). The Origins of Kriging. Mathematical geology 22 (3), 239–252. Xxxxxxx, N. (2015). Statistics for spatial data. Xxxx Xxxxx & Sons.

Bibliography. Xxxxxxx, A., X. Xxxxxx, X. Xxxxxxx, and X. Xxxxxxx (2016). A service requirements engineering method for a digital service ecosystem. Service Oriented Computing and Applications 10 (2), 151–172. Xxxxxxx, A., X. Xxxxxx, and X. Xxxxx (2018). Towards certified open data in digital service ecosystems. Software Quality Journal 26 (4), 1257–1297. Xxxx, X. X., X. X. Xxxx, and X. Xxx (2000). Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (1), 4–37. Xxxxxx, X. (1994, Jan1958). Numerics Report of Gram-Xxxxxxx Orthogonalizationthe committee on methods of clinical examination in electroencephalography. Linear Algebra Electroencephalography and its Applications 197-198Clinical Neurophysiol- ogy 10 (2), 297–316370 – 375. XxxxxxxxXxxxx, X. X., X. Dorigo, and G. Theraulaz (1999). Swarm Intelligence: from Natural to Artificial Systems. Number 1. Oxford university press. Xxxxxxx, X.-F., X. X. Xxxxxxx, X. XxxxxxxxxxXxxxxxxx, and X. X. Xxxxxxxxxxxx Xxxxx (20061998). Numerical OptimizationEfficient global optimization of expensive black-box functions. Journal of Global Optimization 13 (4), 455–492. Xxxxxx, X. and X. Xxxxxx (2005). Classification of multivariate time series and structured data using constructive induction. Machine Learning 58, 179–216. Xxxxxx, C., X. Xxxxxxx, and X. Xxxxxxx (2017). Digital vehicle ecosystems and new business models: Theoretical and Practical AspectsAn overview of digitalization perspectives. Springer Science In Platform Economy & Business MediaModels workshop at i-KNOW ’17. Xxxxxxxxxx, X. (2011). Data mining: Concepts, models, methods, and algorithms (2nd ed.). Hoboken N.J.: Xxxx Xxxxx and IEEE Press. Xxxxxxx, X. and X. Xxxxxxxx (1995). Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks, Volume 4, Perth, Australia, pp. 1942–1948 vol.4. Xxxxxx, R., X. Xxxxx, X. Xxxxx, and X. Xxxxxxx (2017). Connected vehicles and the road to revenue. xxxx://xxxxx-xxx.xxx.xxx/Images/BCG-Connected-Vehicles- and-the-Road-to-Revenue-Dec-2017_tcm9-179631.pdf. Retrieved November 26, 2017. Xxxx, X. and X. Xxxxxxxxxxxx T. Bäck (20042018, Dec). Convex OptimizationMachine learning for predicting the impact point of a low speed vehicle crash. New YorkIn 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), NYOrlando, USA: Cambridge University Press, pp. Xxxxxxx1432–1437. Xxxx, X. (1996). Bagging Predictors. Machine Learning 24 (2), 123–140. Xxxxxxx, X., X. Friedman, X. X. Xxxxx, and X. X. Xxxxxx (1984). Classification and Regression Trees. CRC press. Xxxxxxxxx, D.M., X. Xxxxx, X. XxxxxxXxxxxxx, X. Godau, X. Xxxxxx, and X. Xxxxxxx (2019). A method of determining damage occurring in an accident between a vehicle and a collision partner on the vehicle. World Patent Application WO2019110434A1. Xxxx, M., X. Xxxx, and X. Bäck (2018). Machine learning for predicting the damaged parts of a low speed vehicle crash. In 13th International Conference on Digital Information Management, Berlin, Germany, pp. 179–184. Xxxxxxxx, L., X. Xxxxxxxx, X. X. Xxxx, X. Xxxxxx, and X. Xxxxxx-Xxxxx (2017). Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka. The Journal of Machine Learning Research 18 (25), 1–5. Xxxx, X. X. and X. X. Xxxx (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, UK: Bradford. Xxxxx, X. and X. Xxxxxxxx (2010). Mirrored Sampling Feature selection with the xxxxxx package. Xxxxx, X., X. Xxxxxx, X. Bengio, and Sequential Selection for Evolution StrategiesX. Xxxxxxx (1998). In Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (11), 2278–2324. Xx, X., X. XxxxxxxxRajagopalan, and X. X. Xxxxxxxx (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine 117 (3), 435 – 447. Xxx, J., X. Xxxxx, X. XxxxxxxxxXxxxxxx, and X. Xxxx (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD ’03, New York, USA, pp. 2–11. ACM. Xxx, S., X. Xx, X. Xxxx, X. Xx, and X. X. Xxxxxxx (2017). Creating Autonomous Vehicle Systems. Synthesis Lectures on Computer Science. Xxxxxx & Xxxxxxxx Publishers. Xxxx, X., X. Shelhamer, and X. Xxxxxxx (Eds.2014), Proceedings of the 11th inter- national conference on Parallel problem solving from nature: Part I, PPSN’10, Springer-Verlag, Berlin, pp. 11–21Fully convolutional networks for semantic segmentation. CoRR abs/1411.4038. Xxxxxxx, X. X. (2003). Radial Basis Functions: Theory and Implementations, Volume 12. Cambridge university press. Xxxxxxxx, X., X. X. X. Xxxxxxxx, and X. Xxxxxx (2013, February). A Framework for Evaluating Approximation Methods for Gaussian Process Regression. X. Xxxx. Learn. Res. 14 (1), 333–350. Xxxx, X. and X. Xxx (2009, March). Bagging for Gaussian Process Regression. Xxxxxxxxx, X. and X. Xxxxxxxxxxx (2013). Fast Computation of the Multi-points Expected Improvement with Applications in Batch Selection. In X. Xxxxxxx and X. Xxxxxxxx (Eds.), Learning and Intelligent Optimization, Berlin, Heidelberg, pp. 59–69. Springer Berlin Heidelberg. Xxxxxxx, N. (1990). The Origins of Kriging. Mathematical geology 22 (3), 239–252. Xxxxxxx, N. Xxxxxxxxxx (2015). Statistics for spatial dataAutomotive Ethernet. Xxxx Xxxxx & SonsCambridge University Press.