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2023 | OriginalPaper | Buchkapitel

4. Maschinelles Lernen

verfasst von : Damian Borth, Eyke Hüllermeier, Göran Kauermann

Erschienen in: Künstliche Intelligenz und Data Science in Theorie und Praxis

Verlag: Springer Berlin Heidelberg

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Zusammenfassung

In diesem Kapitel fassen wir die Grundlagen des Maschinellen Lernens (Machine Learning) zusammen.

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Literatur
Zurück zum Zitat Almeida E, Ferreira CA, Gama J (2013) Adaptive model rules from data streams. In: European Conference on Machine Learning and Knowledge Discovery in Databases, ECML/PKDD 2013, Prague, Czech Republic, pp 480–492 Almeida E, Ferreira CA, Gama J (2013) Adaptive model rules from data streams. In: European Conference on Machine Learning and Knowledge Discovery in Databases, ECML/PKDD 2013, Prague, Czech Republic, pp 480–492
Zurück zum Zitat Andrieu C, de Freitas N, Doucet A, Jordan MI (2003) An Introduction to MCMC for Machine Learning. SpringerMATH Andrieu C, de Freitas N, Doucet A, Jordan MI (2003) An Introduction to MCMC for Machine Learning. SpringerMATH
Zurück zum Zitat Bachman P, Sordoni A, Trischler A (2017) Learning algorithms for active learning. In: Proc. ICML, 34th Int. Conf. on Machine Learning, pp 301–310 Bachman P, Sordoni A, Trischler A (2017) Learning algorithms for active learning. In: Proc. ICML, 34th Int. Conf. on Machine Learning, pp 301–310
Zurück zum Zitat Blum AL, Rivest RL (1992) Training a 3-node neural network is np-complete. Neural Networks 5(1):117–127CrossRef Blum AL, Rivest RL (1992) Training a 3-node neural network is np-complete. Neural Networks 5(1):117–127CrossRef
Zurück zum Zitat Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees. Wadsworth Int. Group, Belmont, CAMATH Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees. Wadsworth Int. Group, Belmont, CAMATH
Zurück zum Zitat Carbonell JG, Michalski RS, Mitchell TM (1983) Machine learning: A historical and methodological analysis. AI Magazine 4(3):69–79 Carbonell JG, Michalski RS, Mitchell TM (1983) Machine learning: A historical and methodological analysis. AI Magazine 4(3):69–79
Zurück zum Zitat Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 248–255 Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 248–255
Zurück zum Zitat Doppa JR, Fern A, Tadepalli P (2014) Structured prediction via output space search. Journal of Machine Learning Research 15:1317–1350MathSciNetMATH Doppa JR, Fern A, Tadepalli P (2014) Structured prediction via output space search. Journal of Machine Learning Research 15:1317–1350MathSciNetMATH
Zurück zum Zitat Elsken T, Metzen JH, Hutter F (2019) Neural architecture search: A survey. Journal of Machine Learning Research 20:1–21MathSciNetMATH Elsken T, Metzen JH, Hutter F (2019) Neural architecture search: A survey. Journal of Machine Learning Research 20:1–21MathSciNetMATH
Zurück zum Zitat Fahrmeir L, Tutz G (2001) Multivariate Statistical Modelling Based on Generalized Linear Models. SpringerCrossRefMATH Fahrmeir L, Tutz G (2001) Multivariate Statistical Modelling Based on Generalized Linear Models. SpringerCrossRefMATH
Zurück zum Zitat Feurer M, Klein A, Eggensperger K, Springenberg J, Blum M, Hutter F (2015) Efficient and robust automated machine learning. In: Proc. NIPS, Advances in Neural Information Processing Systems, pp 2962–2970 Feurer M, Klein A, Eggensperger K, Springenberg J, Blum M, Hutter F (2015) Efficient and robust automated machine learning. In: Proc. NIPS, Advances in Neural Information Processing Systems, pp 2962–2970
Zurück zum Zitat Fürnkranz J, Gamberger D, Lavrac N (2012) Foundations of Rule Learning. Springer-VerlagCrossRefMATH Fürnkranz J, Gamberger D, Lavrac N (2012) Foundations of Rule Learning. Springer-VerlagCrossRefMATH
Zurück zum Zitat Gama J (2012) A survey on learning from data streams: current and future trends. Progress in Artificial Intelligence 1(1):45–55CrossRef Gama J (2012) A survey on learning from data streams: current and future trends. Progress in Artificial Intelligence 1(1):45–55CrossRef
Zurück zum Zitat Gauß CF (1809) Theoria motus corporum coelestium in sectionibus conicis solem ambientium Gauß CF (1809) Theoria motus corporum coelestium in sectionibus conicis solem ambientium
Zurück zum Zitat Hastie T, Tisbhirani R, Friedman J (2001) The Elements of Statistical Learning. SpringerCrossRef Hastie T, Tisbhirani R, Friedman J (2001) The Elements of Statistical Learning. SpringerCrossRef
Zurück zum Zitat Hastie TJ, Tibshirani RJ (1990) Generalized Additive Models. Chapman & Hall/CRC Hastie TJ, Tibshirani RJ (1990) Generalized Additive Models. Chapman & Hall/CRC
Zurück zum Zitat Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks 2(5):359–366CrossRefMATH Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks 2(5):359–366CrossRefMATH
Zurück zum Zitat Hühn J, Hüllermeier E (2009) FURIA: An algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery 19:293–319MathSciNetCrossRef Hühn J, Hüllermeier E (2009) FURIA: An algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery 19:293–319MathSciNetCrossRef
Zurück zum Zitat James G, Witten D, Hastie T, Tibshirani R (2017) An Introduction to Statistical Learning. SpringerMATH James G, Witten D, Hastie T, Tibshirani R (2017) An Introduction to Statistical Learning. SpringerMATH
Zurück zum Zitat Kersting K, Lampert C, Rothkopf C (2019) Wie Maschinen lernen. SpringerCrossRef Kersting K, Lampert C, Rothkopf C (2019) Wie Maschinen lernen. SpringerCrossRef
Zurück zum Zitat Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. NIPS, pp 1106–1114 Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. NIPS, pp 1106–1114
Zurück zum Zitat LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4):541–551CrossRef LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4):541–551CrossRef
Zurück zum Zitat McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics 5(4):115–133MathSciNetCrossRefMATH McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics 5(4):115–133MathSciNetCrossRefMATH
Zurück zum Zitat Minsky M, Papert S (1969) Perceptrons: An Introduction to Computational Geometry. MIT press Minsky M, Papert S (1969) Perceptrons: An Introduction to Computational Geometry. MIT press
Zurück zum Zitat Mohr F, Wever M, Hüllermeier E (2018) ML-Plan: Automated machine learning via hierarchical planning. Machine Learning 107(8–10):1495–1515MathSciNetCrossRefMATH Mohr F, Wever M, Hüllermeier E (2018) ML-Plan: Automated machine learning via hierarchical planning. Machine Learning 107(8–10):1495–1515MathSciNetCrossRefMATH
Zurück zum Zitat Narodytska1 N, Ignatiev A, Pereira F, Marques-Silva J (2018) Learning optimal decision trees with SAT. In: Proc. IJCAI, International Joint Conference on Artificial Intelligence Narodytska1 N, Ignatiev A, Pereira F, Marques-Silva J (2018) Learning optimal decision trees with SAT. In: Proc. IJCAI, International Joint Conference on Artificial Intelligence
Zurück zum Zitat Olson RS, Bartley N, Urbanowicz RJ, Moore JH (2016) Evaluation of a tree-based pipeline optimization tool for automating data science. In: Proc. GECCO, Genetic and Evolutionary Computation Conference, Denver, CO, USA, pp 485–492, https://doi.org/10.1145/2908812.2908918 Olson RS, Bartley N, Urbanowicz RJ, Moore JH (2016) Evaluation of a tree-based pipeline optimization tool for automating data science. In: Proc. GECCO, Genetic and Evolutionary Computation Conference, Denver, CO, USA, pp 485–492, https://​doi.​org/​10.​1145/​2908812.​2908918
Zurück zum Zitat Quinlan JR (1979) Discovering rules by induction from large collections of examples. In: Michie D (ed) Expert Systems in the Micro Electronic Age, Edinburgh University Press Quinlan JR (1979) Discovering rules by induction from large collections of examples. In: Michie D (ed) Expert Systems in the Micro Electronic Age, Edinburgh University Press
Zurück zum Zitat Quinlan JR (1986) Induction of decision trees. Machine Learning 1(1):81–106CrossRef Quinlan JR (1986) Induction of decision trees. Machine Learning 1(1):81–106CrossRef
Zurück zum Zitat Quinlan JR (1990) Learning logical definitions from relations. Machine Learning 5:239–266CrossRef Quinlan JR (1990) Learning logical definitions from relations. Machine Learning 5:239–266CrossRef
Zurück zum Zitat Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA
Zurück zum Zitat Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:​1511.​06434
Zurück zum Zitat Robert C, Casella G (2011) A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data. Statistical Science 26(1):102–115MathSciNetCrossRefMATH Robert C, Casella G (2011) A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data. Statistical Science 26(1):102–115MathSciNetCrossRefMATH
Zurück zum Zitat Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65(6):386CrossRef Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65(6):386CrossRef
Zurück zum Zitat Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Tech. rep., California Univ San Diego La Jolla Inst for Cognitive ScienceCrossRef Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Tech. rep., California Univ San Diego La Jolla Inst for Cognitive ScienceCrossRef
Zurück zum Zitat Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric Regression. Cambridge University PressCrossRefMATH Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric Regression. Cambridge University PressCrossRefMATH
Zurück zum Zitat Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al (2015) Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115(3):211–252MathSciNetCrossRef Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al (2015) Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115(3):211–252MathSciNetCrossRef
Zurück zum Zitat Samuel A (1959) Some studies in machine learning using the game of checkers. IBM Journal of Research and Development 44:206–226CrossRef Samuel A (1959) Some studies in machine learning using the game of checkers. IBM Journal of Research and Development 44:206–226CrossRef
Zurück zum Zitat Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Proc. KDD, 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, pp 847–855, https://doi.org/10.1145/2487575.2487629 Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Proc. KDD, 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, pp 847–855, https://​doi.​org/​10.​1145/​2487575.​2487629
Zurück zum Zitat Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30 Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30
Zurück zum Zitat Waegeman W, Dembczynski K, Hüllermeier E (2019) Multi-target prediction: A unifying view on problems and methods. Data Mining and Knowledge Discovery 33(2):293–324MathSciNetCrossRefMATH Waegeman W, Dembczynski K, Hüllermeier E (2019) Multi-target prediction: A unifying view on problems and methods. Data Mining and Knowledge Discovery 33(2):293–324MathSciNetCrossRefMATH
Zurück zum Zitat Wood SN (2017) Generalized Additive Models: An Introduction with R (2nd edition). Taylor & FrancisCrossRef Wood SN (2017) Generalized Additive Models: An Introduction with R (2nd edition). Taylor & FrancisCrossRef
Zurück zum Zitat Yang Q, Zhang Y, Dai W, Pan SJ (2020) Transfer Learning. Cambridge University PressCrossRef Yang Q, Zhang Y, Dai W, Pan SJ (2020) Transfer Learning. Cambridge University PressCrossRef
Zurück zum Zitat Zhou ZH (2012) Ensemble Methods: Foundations and Algorithms. Chapman and Hall Zhou ZH (2012) Ensemble Methods: Foundations and Algorithms. Chapman and Hall
Metadaten
Titel
Maschinelles Lernen
verfasst von
Damian Borth
Eyke Hüllermeier
Göran Kauermann
Copyright-Jahr
2023
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-66278-6_4

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