Machine learning: a review of classification and combining techniques

By S. Kotsiantis, Ioannis D. Zaharakis, P. Pintelas. Artificial Intelligence Review, 2006

1,442 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/36dd3331060e5e6157d9558563b95253308709cb

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In Artificial Intelligence

Citations in the corpus

A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, background): “The choice of a kernel function is very important here [94].”
Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses (2021, background): “…given that AI technologies are particularly known to be well-suited for tasks involving classification, categorization, and pattern recognition (Broussard, 2018; Choudhury, Starr, & Agarwal, 2020; Kotsiantis, Zaharakis, & Pintelas, 2006), and perception may be keenly involved in such tasks.”
Evaluation of machine learning classifiers for mobile malware detection (2014, neutral): “In spite of several machine learning techniques, a review by Kotsiantis et al. (2006) 4.”
Putting Education in “Educational” Apps (2015, background): “The vast advances and remaining challenges in machine learning have taught us about the intricacy of human thinking (e.g., Kotsiantis, Zaharakis, & Pinelas, 2006).”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “leading families of models used in BCIs include linear classifiers, neural networks, non-linear Bayesian classifiers, nearest neighbour classifiers, and classifier combinations [54].”
Design of fusion technique-based mining engine for smart business (2015, methodology): “Classification techniques [22] such as Naive Bayes Classifiers and decision trees learn correlations between a response variable (called class label) and other variables based on empirical probability.”
Machine learning and deep learning (2021, background): “These examples demonstrate that there are different ways of analytical model building, each of them with individual advantages and disadvantages depending on the input data and the derived features (Kotsiantis et al. 2006).”