Books on the topic 'Data classification and machine learning'

Suthaharan, Shan. Machine Learning Models and Algorithms for Big Data Classification. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3.

Pham, Thuy T. Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98675-3.

Jahrestagung, Gesellschaft für Klassifikation. Data analysis, machine learning and applications: Proceedings of the 31st Annual Conference of the Gesellschaft fü̈r Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7-9, 2007. Edited by Preisach Christine. Berlin: Springer, 2008.

Shuurmans, Dale Eric. Effective classification learning. Toronto: University of Toronto, 1996.

Dean, Jared. Big Data, Data Mining, and Machine Learning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118691786.

Friedman, Craig. Utility-based learning from data. Boca Raton: Chapman & Hall/CRC, 2010.

Nicosia, Giuseppe, Panos Pardalos, Giovanni Giuffrida, Renato Umeton, and Vincenzo Sciacca, eds. Machine Learning, Optimization, and Data Science. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13709-0.

Preisach, Christine, Hans Burkhardt, Lars Schmidt-Thieme, and Reinhold Decker, eds. Data Analysis, Machine Learning and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78246-9.

Nicosia, Giuseppe, Panos Pardalos, Giovanni Giuffrida, and Renato Umeton, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72926-8.

Pardalos, Panos M., Piero Conca, Giovanni Giuffrida, and Giuseppe Nicosia, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51469-7.

Nicosia, Giuseppe, Varun Ojha, Emanuele La Malfa, Giorgio Jansen, Vincenzo Sciacca, Panos Pardalos, Giovanni Giuffrida, and Renato Umeton, eds. Machine Learning, Optimization, and Data Science. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64580-9.

Nicosia, Giuseppe, Varun Ojha, Emanuele La Malfa, Giorgio Jansen, Vincenzo Sciacca, Panos Pardalos, Giovanni Giuffrida, and Renato Umeton, eds. Machine Learning, Optimization, and Data Science. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0.

Pardalos, Panos, Mario Pavone, Giovanni Maria Farinella, and Vincenzo Cutello, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27926-8.

Patgiri, Ripon, Sivaji Bandyopadhyay, Malaya Dutta Borah, and Dalton Meitei Thounaojam, eds. Big Data, Machine Learning, and Applications. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62625-9.

Nicosia, Giuseppe, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, and Vincenzo Sciacca, eds. Machine Learning, Optimization, and Data Science. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7.

Myles, White John, ed. Machine learning for hackers. Sebastopol, CA: O'Reilly Media, 2012.

Ao, Sio-Iong. Advances in Machine Learning and Data Analysis. Dordrecht: Springer Netherlands, 2010.

Yu, Shi, Léon-Charles Tranchevent, Bart De Moor, and Yves Moreau. Kernel-based Data Fusion for Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19406-1.

Amouzegar, Mahyar A., ed. Advances in Machine Learning and Data Analysis. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3177-8.

Reddy Edla, Damodar, Pawan Lingras, and Venkatanareshbabu K., eds. Advances in Machine Learning and Data Science. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8569-7.

Sammut, Claude, and Geoffrey I. Webb, eds. Encyclopedia of Machine Learning and Data Mining. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7.

Spiliopoulou, Myra, Lars Schmidt-Thieme, and Ruth Janning, eds. Data Analysis, Machine Learning and Knowledge Discovery. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01595-8.

Hassanien, Aboul Ella, Ashraf Darwish, Sherine M. Abd El-Kader, and Dabiah Ahmed Alboaneen, eds. Enabling Machine Learning Applications in Data Science. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6129-4.

Mola, Francesco, Claudio Conversano, and Maurizio Vichi, eds. Classification, (Big) Data Analysis and Statistical Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-55708-3.

Buntine, Wray. Myths and legends in learning classification rules. Moffett Field, Calif: NASA, Ames Research Center, Research Institute for Advanced Computer Science, 1990.

Statistical learning and data science. Boca Raton: CRC Press, 2012.

Machine learning in image steganalysis. Hoboken: Wiley, 2012.

Utgoff, Paul E. Machine learning of inductive bias. Boston: Kluwer Academic Publishers, 1986.

Kernels for structured data. Singapore: World Scientific, 2008.

Gärtner, Thomas. Kernels for structured data. Hackensack, NJ: World Scientific, 2008.

Eibe, Frank, and Hall Mark A, eds. Data mining: Practical machine learning tools and techniques. 3rd ed. Burlington, MA: Morgan Kaufmann, 2011.

B, Rieger Burghard, Amouzegar Mahyar A, and SpringerLink (Online service), eds. Machine Learning and Systems Engineering. Dordrecht: Springer Science+Business Media B.V., 2011.

Berrar, Daniel. Machine learning methods for analyzing DNA microarray data. [S.l: The Author), 2004.

Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31537-4.

Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23199-5.

Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62416-7.

Pham, Thuy T. Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer, 2019.

Pham, Thuy T. Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer, 2018.

Fletcher, Justin Barrows Swore. A constructive approach to hybrid architectures for machine learning. 1994.

Suthaharan, Shan. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning (Integrated Series in Information Systems). Springer, 2015.

Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series). Springer, 2007.

Dulhare, Uma N., Khaleel Ahmad, and Khairol Amali Bin Ahmad, eds. Machine Learning and Big Data. Wiley, 2020. http://dx.doi.org/10.1002/9781119654834.

Kononenko, Igor, and Matjaž Kukar. Machine learning and data mining. Woodhead Publishing Limited, 2007. http://dx.doi.org/10.1533/9780857099440.

Kroese, Dirk P., Zdravko I. Botev, Thomas Taimre, and Radislav Vaisman. Data Science and Machine Learning. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780367816971.

Machine learning, neural and statistical classification. New York: Ellis Horwood, 1994.

Machine learning, neural and statistical classification. Englewood Cliffs, N.J: Prentice Hall, 1994.

Bilokon, Paul Alexander. Python, Data Science and Machine Learning. WORLD SCIENTIFIC, 2021. http://dx.doi.org/10.1142/11701.

Bhattacharyya, Siddhartha, Hrishikesh Bhaumik, Anirban Mukherjee, and Sourav De, eds. Machine Learning for Big Data Analysis. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110551433.

Ratner, Bruce, Stephen Day, and Christopher Davies. Statistical and Machine-Learning Data Mining. CRC Press, 2011. http://dx.doi.org/10.1201/b11508.

Bhattacharyya, Siddhartha, Sourav De, Anirban Mukherjee, and Hrishikesh Bhaumik. Machine Learning for Big Data Analyis. De Gruyter, Inc., 2018.