Supervised Machine Learning: A Review of Classification Techniques

By S. Kotsiantis. 2007

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Citations in the corpus

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2021, background): “Fully supervised learning, where a task-specific model is trained solely on a dataset of input–output examples for the target task, has long played a central role in many machine learning tasks [60], and natural language processing (NLP) was no exception.”
A survey of data fusion in smart city applications (2019, methodology): “The techniques T4-T8 are associated with data mining [38], [58], where simple input data from multiple sources is fused to generate higher level information enrichment.”
Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “istic patterns by designating a task and its outcome [86].”
Categorical Depth Distribution Network for Monocular 3D Object Detection (2021, background): “Generally, classification is performed by predicting categorical distributions, and encouraging sharpness in the distribution in order to select the correct class [19].”
CatBoost for big data: an interdisciplinary review (2020, methodology): “Since Supervised Machine Learning ( ML ) [1] provides a way to automatically create regression and classification models from labeled datasets, researchers use Supervised ML to model all sorts of phenomena in various fields.”
A Survey of Fake News (2020, background): “It should be noted that as classifiers perform best for ML settings they were initially designed for, it is unjustified to determine algorithms that perform best for fake news detection (related discussions can be found in the work of Fernández-Delgado et al. [2014] and Kotsiantis et al. [2007]).”
Natural language processing in mental health applications using non-clinical texts† (2017, methodology): “There is an extensive machine learning literature on document classification techniques that has been reviewed previously (Sebastiani 2002; Kotsiantis 2007).”
The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “Naive Bayes Naive Bayes is a probabilistic model that assumes that predictors are independent of each other [75].”
Artificial Intelligence Techniques in Smart Grid: A Survey (2021, background): “Supervised learning is the ML task of building general hypotheses for input and output trained by connecting labelled external input and output pairs [11].”
The use of machine learning algorithms in recommender systems: A systematic review (2015, neutral): “Supervised learning (Kotsiantis, 2007; D. Zhang & Tsai, 2006) happens when algorithms are provided with training data and correct answers.”
Supervised Machine Learning for Population Genetics: A New Paradigm (2018, background): “ML is generally divided into two major categories (although hybrid strategies exist): supervised learning [21] and unsupervised learning [22].”
Structured labeling for facilitating concept evolution in machine learning (2014, background): “Labeling Challenges and Solutions in Machine Learning Supervised machine learning requires labeled data for training a machine learner [18].”
Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, neutral): “Only some category of FFNNs that can be applied to PR [76]–[78], that is, not all classes of FFNNmodels can be used for the PR task.”