G
Gail A. Carpenter
Researcher at Boston University
Publications - 161
Citations - 20320
Gail A. Carpenter is an academic researcher from Boston University. The author has contributed to research in topics: Artificial neural network & Adaptive resonance theory. The author has an hindex of 49, co-authored 161 publications receiving 20022 citations. Previous affiliations of Gail A. Carpenter include Massachusetts Institute of Technology & Northeastern University.
Papers
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Journal ArticleDOI
A massively parallel architecture for a self-organizing neural pattern recognition machine
TL;DR: A neural network architecture for the learning of recognition categories is derived which circumvents the noise, saturation, capacity, orthogonality, and linear predictability constraints that limit the codes which can be stably learned by alternative recognition models.
Journal ArticleDOI
Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
TL;DR: The fuzzy ARTMAP system is compared with Salzberg's NGE systems and with Simpson's FMMC system, and its performance in relation to benchmark backpropagation and generic algorithm systems.
PatentDOI
System for self-organization of stable category recognition codes for analog input patterns
TL;DR: ART 2, a class of adaptive resonance architectures which rapidly self-organize pattern recognition categories in response to arbitrary sequences of either analog or binary input patterns, is introduced.
Journal ArticleDOI
Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system
TL;DR: This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, which aims to provide real-time information about concrete mechanical properties such as E-modulus and compressive strength.
Journal ArticleDOI
The ART of adaptive pattern recognition by a self-organizing neural network
TL;DR: Art architectures are discussed that are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns, which opens up the possibility of applying ART systems to more general problems of adaptively processing large abstract information sources and databases.