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

1999

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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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  • Can fly tangential neurons be used to estimate self-motion?

    Publication Year: 1999, Page(s):994 - 999 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (468 KB)

    The so-called tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. This suggests a possible involvement in the self-motion estimation process. In this study, the authors examine whether a simplified matched filter model of these neurons can be used to estimate self-motion from the optic flow. They present a theory for the construction o... View full abstract»

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  • Recognition of gene regulatory sequences by bagging of neural networks

    Publication Year: 1999, Page(s):988 - 993 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (500 KB)

    The authors use an ensemble of multilayer perceptrons to build a model for a type of gene regulatory sequence called a G-box. A variant of the bagging method (bootstrap-and-aggregate) improves the performance of the ensemble over that of a single network. Through a decomposition of the generalization error of the ensemble into bias and variance components, the authors estimate this error from the ... 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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  • KBANNs and the classification of 31P MRS of malignant mammary tissues

    Publication Year: 1999, Page(s):982 - 987 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (468 KB)

    Knowledge-based artificial neural networks (KBANNs) is a hybrid methodology that combines knowledge of a domain in the form of simple rules with connectionist learning. This combination allows the use of small sets of data (typical of medical diagnosis tasks) to train the network. The initial structure is set from the dependencies of a set of rules and it is only necessary to refine these rules by... View full abstract»

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  • Adaptive margin support vector machines for classification

    Publication Year: 1999, Page(s):880 - 885 vol.2
    Cited by:  Papers (10)  |  Patents (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (392 KB)

    We propose a learning algorithm for classification learning based on the support vector machine (SVM) approach. Existing approaches for constructing SVMs are based on minimization of a regularized margin loss where the margin is treated equivalently for each training pattern. We propose a reformulation of the minimization problem such that adaptive margins for each training pattern are utilized, w... 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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  • 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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  • Active topographic mapping of proximities

    Publication Year: 1999, Page(s):952 - 957 vol.2
    Cited by:  Papers (5)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (452 KB)

    We deal with the question of how to reduce the computational costs of obtaining and clustering dissimilarity data. We show that for pairwise clustering, a large portion of the dissimilarity data can be neglected without incurring a serious deterioration of the clustering solution. This fact can be exploited by selecting the dissimilarity values that are supposed to be most relevant in a well-direc... 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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  • Selecting features in neurofuzzy modelling by multiobjective genetic algorithms

    Publication Year: 1999, Page(s):749 - 754 vol.2
    Cited by:  Papers (3)  |  Patents (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (476 KB)

    Empirical modelling in high dimensional spaces is usually preceded by a feature selection stage. Irrelevant or noisy features unnecessarily increase the complexity of the problem and can degrade modelling performance. Here, multiobjective genetic algorithms are proposed as effective means of evolving a diverse population of alternative feature sets with various accuracy/complexity trade-offs. They... View full abstract»

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  • Stimulus segmentation in a stochastic neural network with exogenous signals

    Publication Year: 1999, Page(s):732 - 737 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (364 KB)

    Segmentation by synchrony of firing is investigated in stochastic neural networks with binary neurons. The network is trained by presenting sparsely coded patterns with a Hebbian-type learning rule. Retrieval of these patterns and synchrony of firing is investigated by presenting one or multiple patterns simultaneously to the network. For stimuli consisting of several superimposed patterns the mod... View full abstract»

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  • Regularisation of mixture density networks

    Publication Year: 1999, Page(s):521 - 526 vol.2
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (412 KB)

    Mixture density networks (MDNs) are a well-established method for modelling complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we develop a Bayesian regularisation method for MDNs by an extension of the evidence procedure. The method is tested on two data sets and compared with early stopping View full abstract»

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  • An analysis of initial state dependence in generalized LVQ

    Publication Year: 1999, Page(s):928 - 933 vol.2
    Cited by:  Papers (5)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (348 KB)

    The author proposed a new formulation of learning vector quantisation (LVQ) called generalized LVQ (GLVQ) based on the minimum classification error criterion. In this paper, the initial state dependence in GLVQ is discussed, and it is clarified that the learning rule should be modified to make it insensitive to the initial values of reference vectors. The robustness of the modified GLVQ for the in... View full abstract»

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  • Is a biological temporal learning rule compatible with learning synfire chains?

    Publication Year: 1999, Page(s):551 - 556 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (408 KB)

    The author investigates how a biologically realistic temporal learning rule and the neuronal firing threshold jointly determine the recall speed of a synfire chain trained by sequential activation of its nodes. Numerical analysis of an idealised system of discrete spike response model neurons yields the relationship between threshold and speed of recall, in particular showing that recall is not po... 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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  • A self-organizing map for clustering probabilistic models

    Publication Year: 1999, Page(s):946 - 951 vol.2
    Cited by:  Papers (1)  |  Patents (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (444 KB)

    We present a general framework for self-organizing maps, which store probabilistic models in map units. We introduce the negative log probability of the data sample as the error function and motivate its use by showing its correspondence to the Kullback-Leibler distance between the unknown true distribution of data and our empirical models. We present a general winner search procedure based on thi... 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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  • Backtracking deterministic annealing for constraint satisfaction problems

    Publication Year: 1999, Page(s):868 - 873 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (488 KB)

    We present a deterministic annealing approach to the solution of quadratic constraint satisfaction problems with complex interlocking constraints, such as exemplified in polyomino tiling puzzles. We first analyze the dynamical properties of the solution strategies implemented by deterministic annealing (DA) in the analog neural representation of Potts-mean-field (PMF) and penalty-function-based co... 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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  • Learning error-correcting output codes from data

    Publication Year: 1999, Page(s):743 - 748 vol.2
    Cited by:  Papers (5)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (392 KB)

    A polychotomizer which assigns the input to one of K⩾3 classes is constructed using a set of dichotomizers which assign the input to one of two classes. Defining classes in terms of the dichotomizers is the binary decomposition matrix of size K×L where each of the K⩾3 classes is written as error-correcting output codes (ECOC), i.e., an array of the responses of binary decisions made ... 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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  • Learning to forget: continual prediction with LSTM

    Publication Year: 1999, Page(s):850 - 855 vol.2
    Cited by:  Papers (3)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (448 KB)

    Long short-term memory (LSTM) can solve many tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams without explicitly marked sequence ends. Without resets, the internal state values may grow indefinitely and eventually cause the network to break down. Our remedy is an adaptive “forge... View full abstract»

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  • Rule extraction from binary neural networks

    Publication Year: 1999, Page(s):515 - 520 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (408 KB)

    A new constructive learning algorithm, called Hamming clustering (HC), for binary neural networks is proposed. It is able to generate a set of rules in if-then form underlying an unknown classification problem starting from a training set of samples. The performance of HC has been evaluated through a variety of artificial and real-world benchmarks. In particular, its application in the diagnosis o... View full abstract»

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  • Nonlinear dimensionality reduction with input distances preservation

    Publication Year: 1999, Page(s):922 - 927 vol.2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (300 KB)

    A new error term for dimensionality reduction, which clearly improves the quality of nonlinear principal component analysis neural networks, is introduced, and some illustrative examples are given. The method maintains the original data structure by preserving the distances between data points View full abstract»

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