Abstract
In today’s world, machine learning has gained much popularity, and its algorithms are employed in every field such as pattern recognition, object detection, text interpretation and different research areas. Machine learning, a part of AI (artificial intelligence), is used in the designing of algorithms based on the recent trends of data. This paper aims at introducing the algorithms of machine learning, its principles and highlighting the advantages and disadvantages in this field. It also focuses on the advancements that have been carried out so that the current researchers can be benefitted out of it. Based on artificial intelligence, many techniques have been developed such as perceptron-based techniques and logic-based techniques and also in statistics, instance-based techniques and Bayesian networks. So, overall this paper produces the work done by the authors in the area of machine learning and its applications and to draw attention towards the scholars who are working in this field.
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References
Das, S., Dey, A., Pal, A., Roy, N.: Applications of artificial intelligence in machine learning: review and prospect. Int. J. Comput. Appl. 115(9) (2015)
Angra, S., Ahuja, S.: Machine learning and its applications: a review. In: 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), pp. 57–60. IEEE (2017)
Dey, A.: Machine learning algorithms: a review. Int. J. Comput. Sci. Inf. Technol. 7(3), 1174–1179 (2016)
Kotsiantis, S.B., Zaharakis, I.D., Pintelas, P.E.: Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26(3), 159–190 (2006)
Simon, A., Singh, M.: An overview of M learning and its Ap. Int. J. Electr. Sci. Electr. Sci. Eng. (IJESE) 22 (2015)
Support Vector Machine, https://scikit-learn.org/stable/modules/svm.html. Last accessed 27 Feb 2019
Linear Regression, http://scikitlearn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py. Last accessed 11 May 2018
Logistic regression 3 class-classifier, http://scikitlearn.org/stable/auto_examples/linear_model/plot_iris_logistic.html#sphx-glr-auto-examples-linear-model-plot-iris-logistic-py. Last accessed 11 May 2018
Demonstration of K-means assumption, http://scikitlearn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py. Last accessed 11 May 2018
Deng, L.: Three classes of deep learning architectures and their applications: a tutorial survey. APSIPA Trans. Signal Inf. Process. (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends® Signal Process. 7(3–4):197–387 (2014)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Kaur, R., Juneja, M.A.: Survey of different imaging modalities for renal cancer. Indian J. Sci. Technol. 9, 44 (2016)
Bhatia, N., Rana, M.C.: Deep learning techniques and its various algorithms and techniques. Int. J. Eng. Innov. Res. 4(5) (2015)
Kaur, R., Juneja, M., Mandal, A.K.: A comprehensive review of denoising techniques for abdominal CT images. Multimedia Tools Appl. 77(17), 22735–22770 (2018)
Valenti, R., Sebe, N., Gevers, T., Cohen, I.: Machine learning techniques for face analysis. In: Machine Learning Techniques for Multimedia, pp. 159–187. Springer, Berlin, Heidelberg (2008)
Wang, J., Yuille, A.L.: Semantic part segmentation using compositional model combining shape and appearance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1788–1797 (2015)
Kaur, R., Juneja, M.: Comparison of different renal imaging modalities: an overview. In: Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, pp. 47–57. Springer, Singapore (2018)
Cho, S.B., Won, H.H.: Machine learning in DNA microarray analysis for cancer classification. In: Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003, vol. 19, pp. 189–198. Australian Computer Society, Inc. (2003)
Kaur, R., Juneja, M.: A survey of kidney segmentation techniques in CT images. Curr. Med. Imaging Rev. 14(2), 238–250 (2018)
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Dhall, D., Kaur, R., Juneja, M. (2020). Machine Learning: A Review of the Algorithms and Its Applications. In: Singh, P., Kar, A., Singh, Y., Kolekar, M., Tanwar, S. (eds) Proceedings of ICRIC 2019 . Lecture Notes in Electrical Engineering, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-030-29407-6_5
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DOI: https://doi.org/10.1007/978-3-030-29407-6_5
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