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Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470)

Date 1999

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  • Extended encoding/decoding of embedded structures using connectionist networks

    Publication Year: 1999, Page(s):886 - 891 vol.2
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (472 KB)

    Central to both the symbolist and connectionist approaches to modelling cognition, is the focus of attention on (at least) two important issues; mental representations and mental processes. The choice of representation constrains the choice of process that can be applied to them. In the, so called, classical view on the cognitive architecture, the representations are characterised by the possessio... View full abstract»

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  • Application of a reduced Hopfield neural net on dynamic routing in real time communication network

    Publication Year: 1999, Page(s):637 - 642 vol.2
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (360 KB)

    We propose a virtue token algorithm for finding an optimal route in real time communication network, and use a Hopfield neural net to implement it. Our Hopfield neural net routing method cannot only satisfy the routing requirement of a dynamic communication network, but can also be implemented into hardware. Moreover, our Hopfield neural net can be reduced to a much smaller scale, thus making the ... View full abstract»

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  • SDTs: sparse dynamic trees

    Publication Year: 1999, Page(s):527 - 532 vol.2
    Cited by:  Papers (4)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (456 KB)

    We introduce a class of image models which we call sparse dynamic trees (SDTs). These extend our previous work (1999) on dynamic trees by introducing a top-down generative prior for the tree structures, which leads to a sparse use of nodes in the network. We present results showing the properties of these networks in action on 1D patterns View full abstract»

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  • Time-slicing: a model for cerebellar function based on synchronization, reverberation, and time windows

    Publication Year: 1999, Page(s):539 - 544 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (484 KB)

    We present a new theory of cerebellar function that is based on synchronization, delayed reverberation, and time windows for triggering spikes. We show that granule cells admit messy fiber activity to the parallel fibers only if the Golgi cells are firing synchronously and if the spikes arrive within short and well-defined time windows. The time window mechanism organizes neuronal activity in disc... View full abstract»

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  • Influence of dendritic morphology on axonal competition

    Publication Year: 1999, Page(s):1000 - 1005 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (372 KB)

    The development of nerve connections involves competition among axons for survival promoting factors, or neurotrophins, which are released by the axons' target cells. To study the influence of the target's dendritic tree on axonal competition, the authors have extended their previous model of axonal competition (1999) to take into account the extracellular space around the dendrites. The authors s... View full abstract»

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  • An adaptive support vector regression filter: A signal detection application

    Publication Year: 1999, Page(s):603 - 607 vol.2
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (348 KB)

    A new method for the construction of nonlinear adaptive filters called adaptive support vector regression is introduced for signal detection in noisy environments. A modification of support vector regression for online learning is motivated by the chunking approach and is based on repeated retraining of the filter parameters without the loss of former estimates. Performance of the proposed method ... View full abstract»

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  • Neural-based queueing system modelling for service quality estimation in B-ISDN networks

    Publication Year: 1999, Page(s):970 - 975 vol.2
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (460 KB)

    This paper addresses an original scheme based on feedforward neural networks, for modelling queueing systems fed with busy traffic. A neural network is trained to anticipate the average number of waiting cells, the cell loss rate and the coefficient of variation of the cell inter-departure time, given the mean rate, the peak rate and the coefficient of variation of the cell inter-arrival time. Our... View full abstract»

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  • Effectiveness of feature extraction in neural network architectures for novelty detection

    Publication Year: 1999, Page(s):976 - 981 vol.2
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (476 KB)

    This paper examines the performance of seven neural network architectures in classifying and detecting novel events contained within data collected from turbine sensors. Several different multilayer perceptrons were built and trained using backpropagation, conjugate gradient and quasi-Newton training algorithms. In addition, linear networks, radial basis function networks, probabilistic networks a... View full abstract»

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  • ICANN99 Ninth International Conference on Artificial Neural Networks

    Publication Year: 1999, Page(s):i - ii
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (28 KB)

    First Page of the Article
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  • Contents

    Publication Year: 1999, Page(s):iv - xxv
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (640 KB)

    First Page of the Article
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  • Recursive Bayesian modelling of time series by neural networks

    Publication Year: 1999, Page(s):678 - 683 vol.2
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (416 KB)

    The Bayesian interpretation of regularisation is now well established for batch processing of data by neural networks. However, when the data arrives sequentially the most common approach is still to use least-squares based algorithms. Previous work has suggested the use of Kalman filter based algorithms for training neural networks under sequential learning with regularisation. We examine specifi... View full abstract»

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  • Neural network training using multi-channel data with aggregate labelling

    Publication Year: 1999, Page(s):862 - 867 vol.2
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (420 KB)

    The solution of classification problems using statistical techniques requires appropriately labelled training data. In the case of multi-channel data, however, the labels may only be available in aggregate form rather than as separate labels for each individual-channel. Standard techniques, using a trained model to classify each channel separately, are therefore precluded. We present a method of t... View full abstract»

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  • Rule-extraction from radial basis function networks

    Publication Year: 1999, Page(s):613 - 618 vol.2
    Cited by:  Papers (5)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (400 KB)

    Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classification problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris flower classification task and a vibration diagnosis classification t... View full abstract»

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  • Maximizing information about a noisy signal with a single non-linear neuron

    Publication Year: 1999, Page(s):581 - 586 vol.2
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (484 KB)

    For noise-free information maximization, the output signal entropy must be maximized. This is not true for a noisy input: rather, it must be the difference between this entropy and the residual output uncertainty. A definition of information density is introduced, which provides a discrete local measure of bandwidth efficiency. Novel training rules are proposed which enforce a uniformity of this d... View full abstract»

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  • Response of an excitatory-inhibitory neural network to external stimulation: an application to image segmentation

    Publication Year: 1999, Page(s):803 - 808 vol.2
    Cited by:  Patents (5)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (380 KB)

    Neural network models comprising elements which have exclusively excitatory or inhibitory synapses are capable of a wide range of dynamic behavior, including chaos. In this paper, a simple excitatory-inhibitory neural pair, which forms the building block of larger networks, is subjected to external stimulation. The response shows transition between various types of dynamics, depending upon the mag... View full abstract»

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  • A neural network for scene segmentation based on compact astable oscillators

    Publication Year: 1999, Page(s):690 - 695 vol.2
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (404 KB)

    We show the feasibility of building a neural network for scene segmentation made of astable oscillators. The network is based on Wang and Terman's algorithm, LEGION . However, much simpler astable circuits have substituted the original oscillators so they meet analog microelectronic requirements and can achieve a high integration level. The correct behavior of the modified network and some of its ... View full abstract»

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  • Local minima and plateaus in multilayer neural networks

    Publication Year: 1999, Page(s):597 - 602 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (368 KB)

    Local minima and plateaus pose a serious problem in learning of neural networks. We investigate the geometric structure of the parameter space of three-layer perceptrons in order to show the existence of local minima and plateaus. It is proved that a critical point of the model with H-1 hidden units always gives a critical point of the model with H hidden units. Based on this result, we prove that... View full abstract»

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  • VC dimension bounds for higher-order neurons

    Publication Year: 1999, Page(s):563 - 568 vol.2
    Cited by:  Papers (4)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (448 KB)

    We investigate the sample complexity for learning using higher-order neurons. We calculate upper and lower bounds on the Vapnik-Chervonenkis dimension and the pseudo dimension for higher-order neurons that allow unrestricted interactions among the input variables. In particular, we show that the degree of interaction is irrelevant for the VC dimension and that the individual degree of the variable... View full abstract»

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  • On information maximization and blind signal deconvolution

    Publication Year: 1999, Page(s):702 - 707 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (488 KB)

    We investigate two algorithms for blind signal deconvolution that have been proposed in the literature. We derive a clear interpretation of the information theoretic objective function in terms of signal processing and show that only one is appropriate to solve the deconvolution problem, while the other will only work if the unknown filter is constrained to be minimum phase. Moreover we argue that... View full abstract»

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  • Regularisation of RBF-networks with the Bayesian evidence scheme

    Publication Year: 1999, Page(s):533 - 538 vol.2
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (364 KB)

    We propose a novel regularisation method for Gaussian multiple networks, which adopts a Bayesian approach and draws on the evidence scheme to optimise the hyperparameters. This leads to a new, modified form of the EM algorithm, which is compared with the original scheme on three classification problems View full abstract»

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  • Synergy of spectral and ear model features for neural speech recognition

    Publication Year: 1999, Page(s):785 - 790 vol.2
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (380 KB)

    Hybrid speech recognition systems combining hidden Markov models modeling of speech with neural network ability to discriminate patterns by computing posterior probabilities have reached very good performances. A promising direction to further improve performances is the use of a greater amount of input information. In doing this neural network based models are better than hidden Markov models as ... View full abstract»

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  • IACAPA: modelling recognition and learning of people with an interactive activation and competition model

    Publication Year: 1999, Page(s):779 - 784 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (500 KB)

    Several attempts have been made to model the processes involved in face recognition in a biologically and psychologically plausible manner. The most successful of these attempts used an interactive activation and competition (IAC) architecture. Here, the IAC architecture is extended to model acquisition as well as recognition of people, and the influence of name provision on the learning process i... View full abstract»

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  • Stochastic models for surface information extraction in texts

    Publication Year: 1999, Page(s):892 - 897 vol.2
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (532 KB)

    We describe the application of numerical machine learning techniques to the extraction of information from a collection of textual data. More precisely, we consider the modeling of text sequences with hidden Markov models and multilayer perceptrons and show how these models can be used to perform specific surface extraction tasks (i.e. tasks which do not need in depth syntactic or semantic analysi... View full abstract»

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  • How irrelevant inputs affect MLP pattern based learning

    Publication Year: 1999, Page(s):856 - 861 vol.2
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (368 KB)

    We analyze the effect of irrelevant inputs on pattern based learning dynamics of multilayer perceptrons (MLPs). We propose a model for the evolution of weights linking these irrelevant inputs to hidden network nodes. The model is analyzed for a restricted case and the results are illustrated and discussed View full abstract»

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  • Hypothesis verification based on classification at unequal error rates

    Publication Year: 1999, Page(s):874 - 879 vol.2
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (440 KB)

    We examine the classification of object candidates which are preselected by an automatic segmentation algorithm. The selected candidates are either searched objects (e.g. different traffic signs) or known garbage patterns (e.g. other round objects) or also arbitrary patterns never seen before, since the closed world assumption generally made in classification theory is often violated in practice. ... View full abstract»

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