2017
DOI: 10.1007/978-3-319-63913-0
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An Introduction to Machine Learning

Abstract: the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific … Show more

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Cited by 253 publications
(187 citation statements)
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“…The data sets used for the training phase need to have known labels. The algorithms learn the relationship between the input values and labels and try to predict the output values of the testing data [9]. • Unsupervised Learning: Unsupervised learning deals with problems involving dimensionality reduction used for big data visualisation, feature elicitation, or the discovery of hidden structures.…”
Section: Machine Learningmentioning
confidence: 99%
See 1 more Smart Citation
“…The data sets used for the training phase need to have known labels. The algorithms learn the relationship between the input values and labels and try to predict the output values of the testing data [9]. • Unsupervised Learning: Unsupervised learning deals with problems involving dimensionality reduction used for big data visualisation, feature elicitation, or the discovery of hidden structures.…”
Section: Machine Learningmentioning
confidence: 99%
“…Contrary to supervised learning, in this type, no labels are available. Algorithms in this category try to identify patterns on testing data and cluster the data or predict future values [9]. • Semi-supervised Learning: This is a combination of the previous two categories.…”
Section: Machine Learningmentioning
confidence: 99%
“…For example, in signal processing, popular methods such as kmeans [26] or k-medoids [25] define heuristics based on the minimisation of distance among data points according to some metric. Some other popular methods, such as support vector clustering [14] and traditional machine learning techniques [13], draw on probability distributions, regression, and correlation techniques, providing means for linear separation producing different groups. This includes deep neural networks [48] based on constructing a differentiable landscape on which elements are statistically mapped for classification purposes.…”
Section: Survey Of Related Workmentioning
confidence: 99%
“…Methods for ML regression include least absolute shrinkage and selection operator (Lasso) regression, and support vector regression, among others. The mathematical basis, differences in application, and other modeling considerations related to these methods are beyond the scope of this review (we recommend Hastie et al., ; Kubat, ; Kuhn & Johnson, ; Lantz, ; Mohri, Rostamizadeh, & Talwalkar, ; Shalev‐Shwartz & Ben‐David, ; Zumel & Mount, ).…”
Section: Key Concepts In Machine Learningmentioning
confidence: 99%