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共 197 条记录，以下是181-190 订阅

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pattern classification with noisy features 7th

引用

7th Joint IAPR international workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd international workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Pawlak, M. Siu, D. Department of Electrical and Computer Engineering University of Manitoba Canada Motorola Incorporation Hong Kong

ISBN: (纸本)3540648585

the paper addresses the problem of pattern classification when distortions are present in the observed data. these are distortions occurring either in the process of classifying a test pattern or during the learning phase of a classifier design. In particular we consider the case when the measurements are corrupted by noise. Additive, multiplicative and generalized noise models are taken into account. A unified framework of the posed problems based on the Bayesian paradigm is given. Learning algorithms stemming from nonparametric curve estimation techniques are derived. We examine the effect of distortions in measurements on the proposed classification algorithms. A class of classification techniques being Bayes risk consistent is derived. the latter result makes use of the idea of deconvolution. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Learning algorithms

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pattern classification based on local learning 7th

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7th Joint IAPR international workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd international workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Peng, Jing Bhanu, Bir College of Engineering University of California RiversideCA92521 United States

ISBN: (纸本)3540648585

Local learning methods approximate a target function (a posteriori probability) by partitioning the input space into a set of local regions, and modeling a simple input-output relationship in each one. In order for local learning to be effective for pattern classification in high dimensional settings, regions must be chosen judiciously to minimize bias. this paper presents a novel region partitioning criterion that attempts to minimize bias by capturing differential relevance in input variables in an efficient way. the efficacy of the method is validated using a variety of real and simulated data. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Artificial intelligence

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A pretopological approach for pattern classification with reject options 7th

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7th Joint IAPR international workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd international workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Frélicot, Carl Emptoz, Hubert Université de La Rochelle Laboratoire d’Informatique et d’Imagerie Industrielle La Rochelle Cedex 117042 France I.N.S.A. de Lyon Laboratoire de Reconnaissance des Formes et Vision Villeurbanne Cedex69621 France

ISBN: (纸本)3540648585

In this paper, we present a pretopological approach for pattern classification with reject options. the pretopological approach, based on growing c-neighborhoods, already has proved its efficiency in reducing computation time and storage requirements compared to a k-Nearest Neighbors approach. By including ambiguity and distance reject options, we give to such an approach more adaptability to real classification problems for which classes generally are not clearly separable and/or not completely known. In order to control both types of rejection, the proposed classifier needs a unique parameter to be fixed whereas two parameters generally are necessary (one for each reject type). We also have observed that the behavior of the classifier (depending of the parameter value) with respect to both kinds of rejection is similar to the behavior of well-known rejection-based classifiers proposed so far in the literature. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Nearest neighbor search

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Consistent set estimation in k-dimensions: An efficient approach 7th

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7th Joint IAPR international workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd international workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Chaudhuri, A. Ray Basu, A. Bhandari, S.K. Chaudhuri, B.B. Indian statistical institute 203 B.T. Road Calcutta700 035 India

ISBN: (纸本)3540648585

Determining the shape of a point pattern is a problem of considerable practical interest and has applications in many branches of science related to pattern recognition. Set estimators of a nonparametric nature which may be used as shape descriptors should have several desirable properties. the estimators should be (a) consistent, i.e. Lebesgue measure of the symmetric difference of the actual region and the estimated should go to zero in probability;(b) computationally efficient;and (c) automatic, in the sense that the method should be able to detect the number of independent disjoint components in the region even when this number is unknown. None of the currently known estimators combine all these properties. A new shape descriptor called s-shape in the context of perceived border extraction of dot patterns in 2-D has been recently proposed. Here, a class of set estimators based on the s-shape is developed in k-dimensions, which combine all the above properties. these estimators are consistent not just under the uniform distribution, but also when samples are drawn under any continuous distribution. the order of error in estimation is independent of the dimensionality k. To illustrate the effectiveness of the proposed approach, a linear order algorithm readily derived from the definition is applied in digital domain. the role of t5 that controls the structure of the estimator, is analyzed. © Springer-Verlag Berlin Heidelberg 1998.

关键词： pattern recognition

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A Comparison of Regression, Neural Net, and pattern recognition Approaches to IR 98

A Comparison of Regression, Neural Net, and Pattern Recognit...

引用

CIKM '98: 7th ACM international Conference on Information and knowledge Management - workshop on New Paradigms in Information Visualization (NPIV '98)

作者： Chen, Aitao School of Information Management and Systems 102 South Hall University of California BerkeleyCA94720 United States

ISBN: (纸本)9781581130614

Both neural nets and statistical regression analysis have been applied to information retrieval tasks in the past. In this paper, we propose a new retrieval method that combines Bayes' theorem and statistical density estimation techniques. then we compare the retrieval effectiveness of the logistic regression, linear regression, neural net, linear discriminant, quadratic discriminant, and kernel method that are based on a common set of variables and are trained on a common set of training examples. the experimental results show that the logistic regression, linear regression, neural net, and linear discriminant all performed equally well on the TREC-5 and TREC-6 adhoc test data sets, while the other two retrieval methods, quadratic discriminant and kernel method, performed much worse in comparison to the methods in the first group. the results also indicate that the three-layer neural net achieved slightly better precision than the two-layer neural net when the number of hidden nodes was below four;the performance dropped rapidly when the number of hidden nodes was further increased. Copyright ACM 1998.

关键词： Regression analysis

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Classification and compression of ICEGS using gaussian mixture models

Classification and compression of ICEGS using gaussian mixtu...

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7th IEEE workshop on Neural Networks for Signal Processing (NNSP 97)

作者： Coggins, R Jabri, M Univ of Sydney Sydney

ISBN: (纸本)0780342569

Implantable cardioverter defibrillators (ICD) administer high voltage shock therapies to terminate dangerous cardiac arrhythmias. Improving the functionality of these devices to include on-line diagnosis based on Intracardiac Electrogram (ICEG) morphology and to log dangerous signals is important for their more widespread use. It is essential that the ICD implement a signal compression scheme due to the limited memory in the device. We have fitted gaussian mixture models to the ICEG signals in order to investigate to what extent, non-linear data models are advantageous in this application compared to the traditional linear approaches used in the field and to explore the common features between classification and compression. Results of fitting the mixture models show that typically a single gaussian per class for classifiers and single gaussian prediction models for data compression are adequate data representations provided the data is preprocessed to remove non-stationary behaviour.

关键词： pattern recognition systems

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Digital topologies revisited: An approach based on the topological point-neighbourhood 7th

Digital topologies revisited: An approach based on the topol...

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7th international workshop on Discrete Geometry for Computer Imagery, DGCI 1997

作者： Ptak, Pavel Kofler, Helmut Kropatsch, Walter Center for Machine Perception CMP - Czech Technical University Karlovo nam. 13 Praha 2121 35 Czech Republic Pattern Recognition and Image Proc. Group - PRIP Vienna University of Technology Treitlstr.3 ViennaA-1040 Austria

ISBN: (纸本)3540638849

Adopting the point-neighbourhood definition of topology, which we think may in some cases help acquire a very good insight of digital topologies, we unify the proof technique of the results on 4-connectedness and on 8-connectedness in 2. We also show that there is no topology compatible with 6-connectedness. We shortly comment on potential further use of this approach. © Springer-Vertag Berlin Heidelberg 1997.

关键词： Topology

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Computational complexity reduction in eigenspace approaches 7th

Computational complexity reduction in eigenspace approaches

引用

7th international Conference on Computer Analysis of Images and patterns, CAIP 1997

作者： Leonardis, Aleš Bischof, Horst Vienna University of Technology Institute for Automation Pattern Recognition and Image Processing Group Treitlstraße 3/1832 ViennaA-1040 Austria University of LjubIjana Tržaška 25 LjubljanaSI-1001 Slovenia

ISBN: (纸本)3540634606

Matching of appearance-based object representations using eigenimages is computationally very demanding. Most commonly, to recognize an object in an image, parts of the input image are projected onto the eigenspace and the recovered coefficients indicate the presence or absence of a particular object. In general, the process is sequentially applied to the entire image. In this paper we discuss how to alleviate the problems related to complexity. First, we propose to use a focus-ofattention (FOA) detector which is intended to select candidate areas of interest with minimal computational effort. Only at these areas we then recover the coefficients of eigenimages. Secondly, we propose to employ a multiresolution approach. However, this requires that we depart from the standard way of calculating the coefficients of the eigenimages which relies on the orthogonality property of eigenimages. Instead we calculate them by solving a system of linear equations. We show the results of our approach on real data. © Springer-Verlag Berlin Heidelberg 1997.

关键词： Computers

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6th international workshop on Structural and Syntactical pattern recognition, SSPR 1996

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6th international workshop on Structural and Syntactical pattern recognition, SSPR 1996

ISBN: (纸本)3540615776

the proceedings contain 39 papers. the special focus in this conference is on Grammars, Languages, Morphology and Semantic Nets. the topics include: Efficient recognition of a class of context-sensitive languages described by augmented regular expressions;optimal and information theoretic syntactic pattern recognition for traditional errors;the morphic generator grammatical inference methodology and multilayer perceptrons;a hybrid approach to acoustic modeling;two different approaches for cost-efficient viterbi parsing with error correction;bounded parallelism in array grammars used for character recognition;comparison between the inside-outside algorithm and the viterbi algorithm for stochastic context-free grammars;generalized morphological operators applied to map-analysis;extended cascade-correlation for syntactic and structural pattern recognition;including geometry in graph representations;a quadratic-time graph isomorphism algorithm and its applications;an evidential merit function to guide search in a semantic network based image analysis system;inexact graph matching with genetic search;automatic recognition of bidimensional models learned by grammatical inference in outdoors scenes;signal decomposition by multiscale learning algorithms;structural learning of character patterns for on-line recognition of hand-written Japanese characters;recognition of hand-printed characters using induct machine learning;opponent color processing based on neural models;invariants and fixed structures lead the way to change;representing shape by line patterns;surface skeletonization of volume objects;peculiarities of structural analysis of image contours under various orders of scanning and a structural analysis of curve deformation by discontinuous transformations.

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graph-based representations in pattern recognition 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Walter G. Kropatsch Nicole M. Artner Yll Haxhimusa Xiaoyi Jiang

ISBN: (数字)9783642382215

ISBN: (纸本)9783642382208

this book constitutes the refereed proceedings of the 9th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2013, held in Vienna, Austria, in May 2013. the 24 papers presented in this volume were carefully reviewed and selected from 27 submissions. they are organized in topical sections named: finding subregions in graphs; graph matching; classification; graph kernels; properties of graphs; topology; graph representations, segmentation and shape; and search in graphs.

关键词： pattern recognition Image Processing and Computer Vision Computer Imaging, Vision, pattern recognition and graphics Computer graphics Discrete Mathematics in Computer Science Data Structures

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