Neural networks for classification: a survey

By G. Zhang. IEEE Trans. Syst. Man Cybern. Part C, 2000

1,937 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/67ad9b3f3d91b101909297d79b912532446485c0

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Citations in the corpus

A systematic literature review of machine learning methods applied to predictive maintenance (2019, background): “Details about ANNs and their algorithms can be found in 465 (Zhang, 2000) and (Schmidhuber, 2015) works.”
Context-Aware Generative Adversarial Privacy (2017, methodology): “In keeping with the now common practice in machine learning, in the data-driven approach for GAP, one can use the empirical log-loss function [90,91] given by (9) with”
Support vector machines combined with feature selection for breast cancer diagnosis (2009, background): “It is advantageous to limit the number of input features in a classifier to in order to have a good predictive and less computationally intensive model (Zhang, 2000).”
Machine learning: a review of classification and combining techniques (2006, background): “Zhang (2000) provided an overview of existing work in Artificial Neural Networks (ANNs).”
Optimal approximation of piecewise smooth functions using deep ReLU neural networks (2017, background): “A comprehensive survey on classification by neural networks can be found in [51].”
Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010, background): “There is a vast literature on the design and training of neural-networks for a variety of purposes (see [21] for a review), but this literature is not accessible enough to the non-expert to make neural-networks an “out of the box” technique.”
Digital twin paradigm: A systematic literature review (2021, background): “Artificial Neural Networks (ANN) and Deep Learning (DL) are computing systems that are inspired by the human brain (Zhang, 2000).”
Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction (2012, background): “fication is one of the most successful applications in the ANN approach [23] that can also be adopted for target identification.”
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005, methodology): “…have been successfully applied to bankruptcy prediction (Desai, Conway, & Overstreet, 1997; Elmer & Borowski, 1988; Jensen, 1992; Malhotra & Malhotra, 2002; Markham & Ragsdale, 1995; Patuwo, Hu, & Hung, 1993; Srinivasan & Ruparel, 1990; West, 2000; Zhang, 2000; Zhang, Hu, Patuwo, & Indro, 1999).”
Distinguishing computer graphics from natural images using convolution neural networks (2017, background): “This kind of neural networks show very good results on various classification problems [24].”
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance (2008, background): “031 systems rely on a wide range of classifiers, such as traditional statistical and Bayesian classifiers (Duda, Hart, & Stork, 2000), case-based reasoning classifiers (Aha, Kibler, & Albert, 1991), decision trees (Mitchell, 1997), and neural networks (Zhang, 2000).”
Policy compression for aircraft collision avoidance systems (2016, background): “Predicting the optimal action given a set of inputs is a classification problem often solved with categorical cross entropy loss [17].”