Browsing by Subject "Supervised learning"

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Browsing by Subject "Supervised learning"

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  • Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction
    Carpenter, Gail; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1995-05)
    Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual ...
  • ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network
    Carpenter, Gail A.; Grossbergy, Stephen; Reynolds, John (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)
    This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This ...
  • Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction
    Carpenter, Gail A.; Gaddam, Sai Chaitanya (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2009-04)
    Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved ...
  • Cortical Learning of Recognition Categories: A Resolution of the Exemplar Vs. Prototype Debate
    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? ...
  • dARTMAP: A Neural Network for Fast Distributed Supervised Learning
    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 ...
  • Self-Supervised ARTMAP
    Amis, Gregory P.; Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2009-05)
    Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semisupervised learning). In each case input ...
  • The Specialized Mappings Architecture
    Rosales, Rómer; Sclaroff, Stan (Boston University Computer Science Department, 2003-03-28)
    A probabilistic, nonlinear supervised learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA employs a set of several forward mapping functions that are estimated automatically from training data. ...