Introduction to Machine Learning

By Ethem Alpaydin. American Journal of Orthodontics and Dentofacial Orthopedics, 2018

2,355 citations recorded by the source corpus.

Machine learning refers to the changes in systems that perform tasks associated with artificial intelligence. This chapter presents introduction types and application of machine learning. This chapter also presents the basic concepts related to feature selection techniques such as filter, wrapper and hybrid methods and various machine learning techniques such as artificial neural network, Naive Bayes classifier, support vector machine, k-nearest-neighbor, decision trees, bagging, boosting, random subspace method, random forests, k-means clustering and deep learning. In the last the performance measure of the classifier is presented.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/76473802cd427a8efcc9958ef86f897eccf3af58

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In Control and Systems Engineering

Citations in the corpus

Diagonal rnns in symbolic music modeling (2017, neutral): “Index Terms— Recurrent Neural Networks, Symbolic Music Modeling”
A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability (2009, background): “It is by far the most commonly used metric for assessing the performance of classifiers for years ( Alpaydin 2004; Lim et al. 2000; Witten and Frank 2005).”