Journal ArticleDOI
A connectionist model for category perception: theory and implementation
TLDR
A connectionist model for learning and recognizing objects (or object classes) is presented and the theory of learning is developed based on some probabilistic measures.Abstract:
A connectionist model for learning and recognizing objects (or object classes) is presented. The learning and recognition system uses confidence values for the presence of a feature. The network can recognize multiple objects simultaneously when the corresponding overlapped feature train is presented at the input. An error function is defined, and it is minimized for obtaining the optimal set of object classes. The model is capable of learning each individual object in the supervised mode. The theory of learning is developed based on some probabilistic measures. Experimental results are presented. The model can be applied for the detection of multiple objects occluding each other. >read more
Citations
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Journal ArticleDOI
Classification of heart sounds using an artificial neural network
Tamer Ölmez,Zümray Dokur +1 more
TL;DR: It is observed that HSs of patients are successfully classified by the GAL network compared to the LVQ network.
Journal ArticleDOI
On edge and line linking with connectionist models
TL;DR: Two connectionist models for mid-level vision problems, namely, edge and line linking, have been presented and the experimental results and the proof of convergence of the network models have been provided.
Journal ArticleDOI
IMORL: Incremental Multiple-Object Recognition and Localization
Haibo He,Sheng Chen +1 more
TL;DR: A neural network with a multilayer perceptron (MLP) structure as the base learning model is used and results show the effectiveness of this method in various video stream data sets.
Journal ArticleDOI
Review Neuro-fuzzy computing for image processing and pattern recognition
Sankar K. Pal,Ashish Ghosh +1 more
TL;DR: The relevance of integration of the merits of fuzzy set theory and neural network models for designing an efficient decision making system is explained and feasibility of such systems and different ways of integration are described.
Journal ArticleDOI
PsyCOP-a psychologically motivated connectionist system for object perception
Jayanta Basak,Sankar K. Pal +1 more
TL;DR: A connectionist system has been designed for learning and simultaneous recognition of flat industrial objects by integrating the psychological hypotheses with the generalized Hough transform technique, which uses the mechanism of selective attention for initial hypotheses generation.
References
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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
Toward memory-based reasoning
Craig Stanfill,David L. Waltz +1 more
TL;DR: The intensive use of memory to recall specific episodes from the past—rather than rules—should be the foundation of machine reasoning.
Journal ArticleDOI
Connectionist Models and Their Properties
TL;DR: A general connectionist model is introduced and how it might be used in cognitive science is considered, among the issues addressed are: stability and noise-sensitivity, distributed decision-making, time and sequence problems, and the representation of complex concepts.
Journal ArticleDOI
Distinctive Features, Categorical Perception, and Probability Learning: Some Applications of a Neural Model.
TL;DR: In this article, a model for memory based on neurophysiolo gical considerations is reviewed, where neurons associate two patterns of neural activity by incrementing synaptic connectivity proportionally to the product of pre-and postsynaptic activity, forming a matrix of synaptic connectivities.