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共 135 条记录，以下是81-90 订阅

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Research on the Credit Risk Evaluation and Forecast of Housing Mortgage Loans

Research on the Credit Risk Evaluation and Forecast of Housi...

引用

2nd International Symposium on Intelligent Information Technology Application

作者： Wang, Fei Song, Zhigang Sias Int Univ Sch Business Xinzheng 451150 Peoples R China HNUFE Chenggong Coll Zhengzhou 451200 Peoples R China

ISBN: (纸本)9780769535050

This article aims to analyze the problems of housing mortgage loans. And it establishes an index system of credit risk evaluation according to the differences between developers and lenders. Based on artificial neural network, it establishes a credit risk evaluation and forecast model in housing mortgage loans, which will lay a foundation for the change of credit risk evaluation pattern and for the efficient support in loan decision-making.

关键词： Loans and mortgages Risk analysis artificial neural networks Predictive models Forward contracts Risk management Law Banking Information technology Technology forecasting

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Can't get you out of my head: A connectionist model of cyclic rehearsal

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2nd ZiF Research Group International workshop on Embodied Communication in Humans and Machines: Modeling Communication with Robots and Virtual Humans

作者： Jaeger, Herbert Eck, Douglas Jacobs University Bremen University of Montreal Department of Computer Science Canada

ISBN: (纸本)3540790365

Humans are able to perform a large variety of periodic activities in different modes, for instance cyclic rehearsal of phone numbers, humming a melody sniplet over and over again. These performances are, to a certain degree, robust against perturbations, and it often suffices to present a new pattern a few times only until it can be "picked up". From an abstract mathematical perspective, this implies that the brain, as a dynamical system, (1) hosts a very large number of cyclic attractors, such that (2) if the system is driven by external input with a cyclic motif, it can entrain to a closely corresponding attractor in a very short time. This chapter proposes a simple recurrent neural network architecture which displays these dynamical phenomena. The model builds on echo state networks (ESNs), which have recently become popular in machine learning and computational neuroscience. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Recurrent neural networks

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Dynamic outlier exclusion training algorithm for sequence based predictions in proteins using neural network

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2nd International workshop on pattern recognition in Bioinformatics

作者： Ahmad, Shandar Natl Inst Biomed Innovat Ibaraki Osaka Japan

ISBN: (纸本)9783540752851

Many structural and functional properties of proteins can be described as a one-dimensional one-to-one mapping between residues of protein sequence and target structure or function. These residue level properties (RLPs) have been frequently predicted using neural networks and other machine learning algorithms. Here we present an algorithm to dynamically exclude from the neural network training, examples which are most difficult to separate. This algorithm automatically filters out statistical outliers causing noise and makes training faster without losing network ability to generalize. Different methods of sampling data for neural network training have been tried and their impact on learning has been analyzed.

关键词： binding sites neural networks sequence information outliers

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A two-phase ANN method for genome-wide detection of hormone response elements

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2nd International workshop on pattern recognition in Bioinformatics

作者： Stepanova, Maria Lin, Feng Lin, Valerie C. -L. Nanyang Technol Univ Bioinformat Res Ctr Singapore 637551 Singapore Nanyang Technol Univ Sch Comp Engn Singapore 637551 Singapore Nanyang Technol Univ Sch Biolog Sci Singapore 637551 Singapore

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 neural networks, which can be used for highly specific detection of binding signals. It exploits the capability of a feed-forward neural 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 artificial neural 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.

关键词： neural networks

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pattern recognition in Bioinformatics: Second iapr International workshop, PRIB 2007 Proceedings

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2nd iapr International workshop on pattern recognition in Bioinformatics, PRIB 2007

ISBN: (纸本)9783540752851

The proceedings contain 38 papers. The topics discussed include: automated methods of predicting the function of biological sequences using GO and rough set;a two-phase ANN method for genome-wide detection of hormone response elements;an expert knowledge-guided mutation operator for genome-wide genetic analysis using genetic programming;strong GC and AT skew correlation in chicken genome;using decision templates to predict subcellular localization of protein;generalized schemata theorem incorporating twin removal for protein structure prediction;using fuzzy support vector machine network to predict low homology protein structural classes;protein fold recognition based upon the amino acid occurrence;using efficient RBF network to identify interface residues based on PSSM profiles and biochemical properties;and dynamic outlier exclusion training algorithm for sequence based predictions in proteins using neural network.

关键词： Bioinformatics

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A framework for path analysis in gene regulatory networks

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2nd iapr International workshop on pattern recognition in Bioinformatics, PRIB 2007

作者： Ram, Esh Chetty, Madhu Gippsland School of IT Monash University Churchill Vic. 3842 Australia

ISBN: (纸本)9783540752851

The inference of a network structure from microarray data providing dynamical information about the underlying Gene regulatory network is an important and still outstanding problem. Recently, a causal modeling approach was presented in our publications to recover the structure of this network. However, issues like spurious arcs and time delay were not dealt with previously. The graph-theoretical measure d-separation provides criteria to recover the network structure edge-by-edge by calculating the partial correlation. However, the estimation of partial correlations from small sample sizes is a practical problem. As our approach attempts to find networks that closely match the observed partial correlation constraints in the data, main aim to this paper is to attempt to maximize the scoring metric used. In this paper, we formulate a framework for path analysis as a post processing step after learning gene regulatory network using causal modeling. The approach is tested with both artificial and real gene regulatory network scenario and the structure recovered after post processing better fits the data. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Gene expression

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Comparative analysis of a hierarchical bayesian method for quantitative trait loci analysis for the arabidopsis thaliana

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2nd iapr International workshop on pattern recognition in Bioinformatics, PRIB 2007

作者： Pearson, Caroline Simmons, Susan J. Ricanek Jr., Karl Boone, Edward L. University of North Carolina Wilmington Department of Mathematics and Statistics 601 South College Road Wilmington NC 28403 United States University of North Carolina Wilmington Department of Computer Science 601 South College Road Wilmington NC 28403 United States Virginia Commonwealth University Department of Statistical Sciences and Operations Research Richmond VA 23284 United States

ISBN: (纸本)9783540752851

This work performs an analysis on two, quite different, techniques for Quantitative Trait Loci (QTL) Analysis. Interval Mapping (IM) as described by Karl Broman is compared to a Hierarchical Bayesian Model (HBM) technique that reduces the problem of QTL analysis down to one of model selection. Simulations were generated for the flowering plant of the Arabidopsis thaliana for evaluation of the techniques. It is shown that the HBM technique was much more successful at determining the appropriate loci/markers and corresponding chromosomes than the IM technique given a single loci. It was further elucidated through simulation runs that the HBM was robust against two loci/markers, whereas IM completely failed. The contribution of this work is in the comparison and analysis of the IM method to that of the HBM;hence, demonstrating through simulations that the HBM technique is superior to that of the IM for the Arabidopsis simulated data. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Bayesian networks

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Orientation histograms for face recognition

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2nd iapr workshop on artificial neural networks in pattern recognition

作者： Schwenker, Friedhelm Sachs, Andreas Palm, Guenther Kestler, Hans A. Univ Ulm Dept Neural Informat Proc D-89069 Ulm Germany

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.

关键词： orientation histograms face recognition

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Multiple neural networks for facial feature localization in orientation-free face images

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2nd iapr workshop on artificial neural networks in pattern recognition

作者： Prevost, Lionel Belaroussi, Rachid Milgram, Maurice Univ Paris 06 PRC F-75005 Paris France

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%.

关键词： Multilayer neural networks

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An experimental study on training radial basis functions by gradient descent

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2nd iapr workshop on artificial neural networks in pattern recognition

作者： Torres-Sospedra, Joaquin Hernandez-Espinosa, Carlos Fernandez-Redondo, Mercedes Univ Jaume 1 Dept Ingn & Ciencia Comp Castellon de La Plana 12071 Spain

ISBN: (纸本)3540379517

In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neural networks. 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.

关键词： neural networks

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