The Multiple Classifier Systems are nowadays one of the most promising directions in patternrecognition. There are many methods of decision making by the ensemble of classifiers. The most popular are methods that hav...
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ISBN:
(纸本)9783540876557
The Multiple Classifier Systems are nowadays one of the most promising directions in patternrecognition. There are many methods of decision making by the ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers' decisions. This work presents methods of classifier combination, where neuralnetworks plays a role of fuser block. Fusion on level of recognizer responses or values of their discriminant functions is applied. The qualities of proposed methods are evaluated via computer experiments on generated data and two benchmark databases.
The ability to store and retrieve information is critical in any type of neural network. In neural network, the memory particularly associative memory, can be defined as the one in which the input pattern leads to the...
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ISBN:
(纸本)9789728865863
The ability to store and retrieve information is critical in any type of neural network. In neural network, the memory particularly associative memory, can be defined as the one in which the input pattern leads to the response of a stored pattern (output vector) that corresponds to the input vector. During the learning phase the memory is fed with a number of input vectors that it learns and remembers and in the recall phase when some known input is presented to it, the network exactly recalls and reproduces the required output vector. In this paper, we improve and increase the storing ability of the memory model proposed in[1]. Besides, we show that there are certain instances where the algorithm in[1] does not produce the desired performance by retrieving exactly the correct vector from the memory. That is, in their algorithm, a number of output vectors can become activated from the stimulus of an input vector while the desired output is just a single correct vector. We propose a simple solution that overcomes this and can uniquely and correctly determine the output vector stored in the associative memory when an input vector is applied. Thus we provide a more general scenario of this neural network memory model consisting of memory element called Competitive Cooperative Neuron (CCN).
Steroid hormone receptors compose a subgroup of regulatory proteins which tend to recognize partially symmetric response elements on DNA. Identification of the members of a gene regulatory machine conducted by steroid...
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ISBN:
(纸本)9783540752851
Steroid hormone receptors compose a subgroup of regulatory proteins which tend to recognize partially symmetric response elements on DNA. Identification of the members of a gene regulatory machine conducted by steroid hormones could provide better understanding of nature and development of diseases. We present an approach based on a succession of neuralnetworks, which can be used for highly specific detection of binding signals. It exploits the capability of a feed-forwardneural network to model datasets with high confidence, while a recurrent network grants putative response elements with biologically meaningful structures. We have used a novel method to train such a two-phase artificialneural network with a set of experimentally validated response elements for steroid hormone receptors. We have demonstrated that sequence-based prediction followed by structure-based classification of putative binding sites allows to eliminate large amount of false positives. An implementation of the neural network with Field-Programmable Gate Array is also briefly described.
neural network models for unsupervised patternrecognition learning are challenged when the difference between the patterns of the training set is small. The standardneural network architecture for pattern recognitio...
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ISBN:
(纸本)3540379517
neural network models for unsupervised patternrecognition learning are challenged when the difference between the patterns of the training set is small. The standardneural network architecture for patternrecognition learning consists of adaptive forward connections. and lateral inhibition, which provides competition between output neurons. We propose an additional adaptive inhibitory feedback mechanism, to emphasize the difference between training patterns and improve learning. We present an implementation of adaptive feedback inhibition for spiking neural network models, based on spike timing dependent plasticity (STDP). When the inhibitory feedback connections are adjusted using an anti-Hebbian learning rule, feedback inhibition suppresses the redundant activity of input units which code the overlap between similar stimuli. We show, that learning speed and pattern discriminatability can be increased by adding this mechanism to the standard architecture.
We present in this paper a new facial feature localizer. It uses a kind of auto-associative neural network trained to localize specific facial features (like eyes and mouth corners) in orientation-free faces. One poss...
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ISBN:
(纸本)3540379517
We present in this paper a new facial feature localizer. It uses a kind of auto-associative neural network trained to localize specific facial features (like eyes and mouth corners) in orientation-free faces. One possible extension is presented where several specialized detectors are trained to deal with each face orientation. To select the best localization hypothesis, we combine radiometric and probabilistic information. The method is quite fast and accurate. The mean localization error (estimated on more than 700 test images) is lower than 9%.
In this paper we present a method to recognize human faces based on histograms of local orientation. Orientation histograms were used as input feature vectors for a k-nearest neigbour classifier. We present a method t...
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ISBN:
(纸本)3540379517
In this paper we present a method to recognize human faces based on histograms of local orientation. Orientation histograms were used as input feature vectors for a k-nearest neigbour classifier. We present a method to calculate orientation histograms of n x n subimages partitioning the 2D-camera image with the segmented face. Numerical experiments have been made utilizing the Olivetti Research Laboratory (ORL) database containing 400 images of 40 subjects. Remarkable recognition rates of 98% to 99% were achieved with this extremely simple approach.
In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neuralnetworks. In particular we compare the classical training which consist of an unsupervised training...
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ISBN:
(纸本)3540379517
In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neuralnetworks. In particular we compare the classical training which consist of an unsupervised training of centers followed by a supervised training of the weights at the output, with the full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare Batch training with Online training and we conclude that Online training suppose a reduction in the number of iterations.
Estimation of probability density functions (pdf) is one major topic in patternrecognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric tec...
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ISBN:
(纸本)3540379517
Estimation of probability density functions (pdf) is one major topic in patternrecognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effective, PW suffers from several limitations. artificialneuralnetworks (ANN) are, in principle, an alternative family of nonparametric models. ANNs are intensively used to estimate probabilities (e.g., class-posterior probabilities), but they have not been exploited so far to estimate pdfs. This paper introduces a simple neural-based algorithm for unsupervised, nonparametric estimation of pdfs, relying on PW. The approach overcomes the limitations of PW, possibly leading to improved pdf models. An experimental demonstration of the behavior of the algorithm w.r.t. PW is presented, using random samples drawn from a standard exponential pdf.
Automatic eye tracking is a challenging task, with numerous applications in biometrics, security, intelligent human-computer interfaces;and driver's sleepiness detection systems. Eye localization and extraction is...
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ISBN:
(纸本)3540379517
Automatic eye tracking is a challenging task, with numerous applications in biometrics, security, intelligent human-computer interfaces;and driver's sleepiness detection systems. Eye localization and extraction is, therefore, the first step to the solution of such problems. In this paper, we present a new method, based on neural autoassociators, to solve the problem of detecting eyes from a facial image. A subset of the AR Database, collecting individuals both with or without glasses and with open or closed eyes, has been used for experiments and benchmarking. Preliminary experimental results are very promising and demonstrate the efficiency of the proposed eye localization system.
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