nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

Intelligent Data Analysis Techniques—Machine Learning and Data Mining

verfasst von : Dan Simovici

Erschienen in: Artificial Intelligent Approaches in Petroleum Geosciences

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This introductory chapter presents some of the main paradigms of intelligent data analysis provided by machine learning and data mining. After discussing several types of learning (supervised, unsupervised, semi-supervised, active and reinforcement learning) we examine several classes of learning algorithms (naive Bayes classifiers, decision trees, support vector machines, and neural networks) and the modalities to evaluate their performance. Examples of specific applications of algorithms are given using System R.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nächstes Kapitel On Meta-heuristics in Optimization and Data Analysis. Application to Geosciences

Abu-Mostafa YS, Magdon-Ismail M, Lin HT (2012) Learning from data. AML Book, AMLbook.com

Anderson E (1936) The species problem in iris. Ann Mo Bot Gard 23:457–509CrossRef

Bishop CM (2007) Pattern recognition and machine learning. Springer, New York

Blumer A, Ehrenfeucht A, Haussler D, Warmuth MK (1989) Learnability and the vapnik-chervonenkis dimension. J ACM 36(4):929–965CrossRefMATHMathSciNet

Breiman L, Friedman JH, Olshen RO, Stone CS (1998) Classification and regression trees. Chapman and Hall, Boca Raton (reprint edition)

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

Cristianini N, Shawe-Taylor J (2000) Support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRef

Dasgupta S (2011) Two faces of active learning. Theoret Comput Sci 412:1767–1781CrossRefMATHMathSciNet

Drucker H, Burges CJC, Kaufman L, Smola AJ, Vapnik V (1996) Support vector regression machines. In: Advances in neural information processing systems 9, NIPS, Denver, CO, USA, 2–5 Dec 1996, pp 155–161

Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7:179–188CrossRef

Freund Y, Shapire RE (1999) Large margin classification using the perceptron algorithm. Mach Learn 37:277–296CrossRefMATH

Günther F, Fritsch S (2010) Neuralnet: training of neural networks. R J 2(1):30–38

Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) kernlab—an s4 package for kernel methods in R. J Stat Softw 11:1–20

Karatzoglu A, Meyer DM, Hornik K (2006) Support vector machines in R. J Stat Softw 15:1–28

Kung SY (2014) Kernel methods and machine learning. Cambridge University Press, CambridgeCrossRefMATH

Lander J (2014) R for everyone. Addison-Wesley, Upper Saddle River

Lantz B (2013) Machine learning with R. PACKT Publishing, Birmingham

Lewis DD, Gale WA (1994) A sequential algorithm for training text classifiers. In: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR’94, pp 3–12. Springer-Verlag New York, Inc, New York

Maindonald J, Braun J (2004) Data analysis and graphics using R—an example-based approach. Cambridge University Press, Cambridge

Matloff N (2011) The art of R programming—a tour of statistical software design. No starch press, San Francisco

McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophy 5:115–133CrossRefMATHMathSciNet

Mitchell TM (1997) Machine learning. McGraw-Hill, BostonMATH

Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of machine learning. MIT Press, CambridgeMATH

Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge

Novikoff ABJ (1962) On convergence proofs on perceptrons. In: Proceedings of the symposium on mathematical theory of automata 12:615–622

Pitts W, McCulloch WS (1947) How we know universals—the perception of auditory and visual forms. Bull Math Biophys 9:127–147CrossRef

Quinlan JR (1993) C 4.5 programs for machine learning. Morgan Kaufmann Publ., San Mateo

Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386–407CrossRefMathSciNet

Scheffer T, Decomain C, Wrobel S (2001) Active hidden Markov models for information extraction. In: Advances in intelligent data analysis, 4th international conference, IDA 2001. Cascais, Portugal, Sept 13–15, 2001. Proceedings, pp 309–318

Schohn G, Cohn D (2000) Less is more: active learning with support vector machines. In: Proceedings of the seventeenth international conference on machine learning (ICML 2000), Stanford University, Stanford, CA, June 29–July 2, 2000, pp 839–846

Schütze H, Velipasaoglu E, Pedersen JO (2006) Performance thresholding in practical text classification. In: Proceedings of the 2006 ACM CIKM international conference on information and knowledge management, Arlington, 6–11 Nov 2006, pp 662–671

Settles B (2012) Active learning. Morgan and Claypool

Shalev-Shwartz S, Ben-David S (2014) Understanding machine learning. Cambridge University Press, CambridgeCrossRefMATH

Shao Y, Cen Y (2014) Data mining applications with R. Academic Press, San Diego

Shawe-Taylor J, Cristianini N (2005) Kernel methods for pattern analysis. Cambridge University Press, Cambridge

Simovici DA, Djeraba C (2014) Mathematical tools for data mining, 2nd edn. Springer, LondonCrossRefMATH

Statnikov A, Aliferis CF, Hardin DP, Guyon I (2011) A gentle introduction to support vector machines in biomedicine. World Scientific, SingaporeCrossRef

Steinwart I, Christman A (2008) Support vector machines. Springer, Berlin

Suykens JAK, van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2005) Least squares support vector machines. World Scientific, New Jersey

Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRefMathSciNet

Velipasaoglu E, Schütze H, Pedersen JO (2007) Improving active learning recall via disjunctive boolean constraints. In SIGIR 2007: proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, Amsterdam, The Netherlands, July 23–27, 2007, pp 893–894

Wickham H (2009) ggplot2—Elegant graphics for data analysis. Springer, DordrechtMATH

Witten IH, Frank E, Hall MA (2011) Data mining—practical machine learning tools and techniques, 3rd edn. Elsevier (Morgan Kaufmann), Amsterdam

Zaki MJ, Meira WM (2014) Data mining and analysis. Cambrige University Press, CambrigeMATH

Zhao Y (2013) R and data mining—example and case studies. Academic Press, San Diego

Titel: Intelligent Data Analysis Techniques—Machine Learning and Data Mining
verfasst von: Dan Simovici
Verlag: Springer International Publishing
Buch: Artificial Intelligent Approaches in Petroleum Geosciences
Print ISBN: 978-3-319-16530-1

Electronic ISBN: 978-3-319-16531-8

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-16531-8_1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"