Journal ArticleDOI
A connectionist model for category perception: theory and implementation
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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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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.
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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.
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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.
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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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Book
Connectionist models and their properties
TL;DR: In this article, the authors introduce a general connectionist model and consider how it might be used in cognitive science, including stability and noise-sensitivity, distributed decision-making, time and sequence problems, and representation of complex concepts.
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Neural theory of association and concept-formation
TL;DR: Primitive neural models of association and concept-formation are presented, which will elucidate the distributed and multiply superposed manner of retaining knowledge in the brain.
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Dynamic connections in neural networks
TL;DR: The purpose of this paper is to extend the methodology to cover several aspects of change and memory in massively parallel networks.
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Neural network model for selective attention in visual pattern recognition and associative recall.
TL;DR: An improved version of the earlier model of selective attention, where the ability of segmentation is improved by lateral inhibition and the model can recall the complete pattern in which the noise has been eliminated and the defects corrected.