Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps

By Gail A. Carpenter, Stephen Grossberg, Natalya Markuzon, John H. Reynolds, D. B. Rosen. 1992

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Citations in the corpus

Memory Efficient Experience Replay for Streaming Learning (2018, background): “ARTMAP networks [11], [12], [13], [71] are another approach and they learn faster.”
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, methodology): “There have been shallow neural networks designed for streaming learning, including ARTMAP networks [7, 8, 49]; however, ARTMAP is sensitive to the order in which training data is presented and it is not capable of representation learning.”
Adaptive resonance theory (1997, methodology): “ARTMAP models (Carpenter et al., 1992) combine two unsupervised modules to carry out supervised leaming.”
Structure-Adaptable Digital Neural Networks (1999, unknown): “1 ART-based arti cial neural network models The ART theory has led to an evolving series of arti cial neural network models that perform supervised and unsupervised learning [34, 35, 36, 38, 40, 41, 42] (Figure 4.”
A reinforcement learning framework for parameter control in computer vision applications (2004, methodology): “The Fuzzy ARTMAP is also much less sensitive to algorithm parameters.”
Online Learning: A Comprehensive Survey (2018, background): “In literature, there are quite a lot of different studies more or less concerned with adaptive learning [210, 307], which attempt to adapt a learning model/system (e.”
Learn++: an incremental learning algorithm for supervised neural networks (2001, unknown): “One notable exception is the (fuzzy) ARTMAP algorithm [13], [14], which”
Supervised learning of fuzzy ARTMAP neural networks through particle swarm optimization (2007, methodology): “ARTMAP refers to a family of neural network architectures based on Adaptive Resonance Theory (ART) [4] that is capable of fast, stable, on-line, unsupervised or supervised, incremental learning, classification, and prediction [6] [7].”
Survey of clustering algorithms (2005, unknown): “Use crossover (two-point crossover) and mutation (bit-wise mutation) operator to generate offspring from the individuals chosen in step 2).”
Biased ART: A neural architecture that shifts attention toward previously disregarded features following an incorrect prediction (2010, unknown): “…derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks, including fuzzy ARTMAP (Carpenter et al., 1992), ARTMAP-IC (Carpenter & Markuzon, 1998), and Gaussian ARTMAP (Williamson, 1996).”
Clustering: A neural network approach (2010, unknown): “…ART 1-based clustering algorithms are the improved ART 1 (IART 1) (Shih, Moh, & Chang, 1992), the adaptive Hamming net (AHN) (Hung & Lin, 1995), the fuzzy ART (Carpenter et al., 1991a, 1992; Carpenter & Ross, 1995), the fuzzy AHN (Hung & Lin, 1995), and the projective ART (PART) (Cao &Wu, 2002).”
Evolving Fuzzy-Rule-Based Classifiers From Data Streams (2008, background): “, adaptive resonance theory (ART) [34], incremental learning vector quantizers (iLVQs)], probabilistic [e.”