Browsing College of Arts and Sciences by Author "Carpenter, Gail A."

OpenBU

Browsing College of Arts and Sciences by Author "Carpenter, Gail A."

Sort by: Order: Results:

  • Carpenter, Gail A.; Grossberg, Stephen; Iizuka, Kunihiko (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1992-02)
    This article compares the performance of Fuzzy ARTMAP with that of Learned Vector Quantization and Back Propagation on a handwritten character recognition task. Training with Fuzzy ARTMAP to a fixed criterion used many ...
  • Carpenter, Gail A.; Gaddam, Chaitanya Sai; Mingolla, Ennio (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2007-09)
    CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial ...
  • Amis, Gregory P.; Carpenter, Gail A.; Ersoy, Bilgin; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2009-03)
    Do humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex? ...
  • Carpenter, Gail A.; Milenova, Boriana L.; Noeske, Benjamin W. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1997-12)
    Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid ...
  • Amis, Gregory P.; Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2007-01)
    Default ARTMAP combines winner-take-all category node activation during training , distributed activation during testing, and a set of default parameter values that define a ready-to-use, general-purpose neural network ...
  • Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-02)
    Adaptive resonance theory (ART) models have been used for learning and prediction in a wide variety of applications. Winner-take-all coding allows these networks to maintain stable memories, but this type of code representation ...
  • Carpenter, Gail A.; Milenova, Boriana L. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1999-05)
    Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid ...
  • Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-02)
    Adaptive resonance theory (ART) models have been used for learning and prediction in a wide variety of applications. Winner-take-all coding allows these networks to maintain stable memories, but this type of code representation ...
  • Carpenter, Gail A.; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)
  • Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-01)
    A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning ...
  • Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-03)
    The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a ...
  • Carpenter, Gail A.; Govindarajan, Krishna (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)
    A procedure that uses fuzzy ARTMAP and K-Nearest Neighbor (K-NN) categorizers to evaluate intrinsic and extrinsic speaker normalization methods is described. Each classifier is trained on preprocessed, or normalized, vowel ...
  • Asfour, Yousif R.; Carpenter, Gail A.; Grossberg, Stephen; Lesher, Gregory W. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-08)
    Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. ...
  • Asfour, Yousif R.; Carpenter, Gail A.; Grossberg, Stephen; Lesher, Gregory (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)
    Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ...
  • Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-12-15)
    Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP synthesize fuzzy logic and ART networks ...
  • Carpenter, Gail A.; Gjaja, Marin N. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-12)
    Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART ...
  • Carpenter, Gail A.; Grossberg, Stephen; Markuzon, Natalya; Reynolds, John H.; Rosen David B. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-08)
    A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, ...
  • Carpenter, Gail A.; Grossberg, Stephen; Reynolds, John H. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1994-10)
    An incremental, nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is introduced. In slow-learning mode, fuzzy ARTMAP searches for patterns of data on which to build ever more accurate ...
  • Carpenter, Gail A.; Grossberg, Stephen; Reynolds, John H. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)
    A nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is here described. Because the procedure does not make a priori assumptions about underlying probability distributions, it yields ...
  • Carpenter, Gail A.; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)

Search OpenBU


Advanced Search

Browse

Deposit Materials