Abstract
Machine learning task is broadly divided into supervised and unsupervised approaches. In supervised learning, output is already known and we have to train the model by giving lot of data called labeled dataset to train our model. The main goal is to predict the outcome. It includes regression and classification problem. In unsupervised learning, no output map** with input as well as it is independent in nature. The dataset used in unsupervised machine learning is unlabeled. The main focus of this paper is to give detailed understanding of supervised and unsupervised machine learning algorithm with pseudocodes.
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References
Bonaccorso G (2017) Machine learning algorithms. Packt Publishing Ltd.
Goodfellow I, Bengio Y, Courville A (2016) Machine learning basics. Deep Learn 1(7):98–164
Dietterich TG (1997) Machine-learning research. AI magazine 18(4):97–97
El Naqa I, Murphy MJ (2015) What is machine learning? In: Machine learning in radiation oncology. Springer, pp 3–11
K¨ording KP, K¨onig P (2001) Supervised and unsupervised learning with two sites of synaptic integration. J Comput Neurosci 11(3):207–215
Arunraj NS, Hable R, Fernandes M, Leidl K, Heigl M (2017) Comparison of super- vised, semi-supervised and unsupervised learning methods in network intrusion detection system (nids) application. Anwendungen und Konzepte der Wirtschaftsinformatik 6
Chen L, Zhai Y, He Q, Wang W, Deng M (2020) Integrating deep supervised, self- supervised and unsupervised learning for single-cell RNA-seq clustering and annotation. Genes 11(7):792
ButlerKT, Davies DW, Cartwright H, Isayev O, Walsh A (2018) Machine learning for molecular and materials science. Nature 559(7715):547–555
Liu W, Chawla S, Cieslak DA, Chawla NV (2010) A robust decision tree algorithm for imbalanced data sets. In: Proceedings of the 2010 SIAM international conference on data mining. SIAM, pp 766–777
Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260
Manwani N, Sastry PS (2011) Geometric decision tree. IEEE Trans Syst Man Cybern Part B Cybern 42(1):181–192
Ayodele TO (2010) Types of machine learning algorithms. New Adv Mach Learn 3:19–48
Wei J, Chu X, Sun X-Y, Kun Xu, Deng H-X, Chen J, Wei Z, Lei M (2019) Machine learning in materials science. InfoMat 1(3):338–358
Lee K, Caverlee J, Webb S (2010) Uncovering social spammers: social honeypots+ machine learning. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, pp 435–442
Witten IH, Frank E, Hall MA, Pal CJ (2005) Mining data: Practical machine learning tools and techniques. In: Data Mining 2, p 4
Tom M Mitchell. Does machine learning really work? AI magazine, 18(3):11–11, 1997.
Mohri M, Rostamizadeh A, Talwalkar A (2018) Foundations of machine learning. MIT press
Raschka S (2015) Python machine learning. Packt publishing Ltd.
Zhou Z-H (2016) Learnware: on the future of machine learning. Front Comput Sci 10(4):589–590
Hilas CS, Mastorocostas PA (2008) An application of supervised and unsupervised learning approaches to telecommunications fraud detection. Knowl Based Syst 21(7):721–726
Oral M, Oral EL, Aydın A (2012) Supervised versus unsupervised learning for construction crew productivity prediction. Autom Constr 22:271–276
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Esther Varma, C., Prasad, P.S. (2023). Supervised and Unsupervised Machine Learning Approaches—A Survey. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2021. Lecture Notes in Electrical Engineering, vol 947. Springer, Singapore. https://doi.org/10.1007/978-981-19-5936-3_7
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DOI: https://doi.org/10.1007/978-981-19-5936-3_7
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