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Global feedforward neural network learning for classification and regression 3rd

引用

3rd International workshop on Energy Minimization Methods in Computer Vision and pattern recognition, EMMCVPR 2001

作者： Toh, Kar-Ann Lu, Juwei Yau, Wei-Yun Centre for Signal Processing School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore

ISBN: (纸本)3540425233

This paper addresses the issues of global optimality and training of a Feedforward neural Network (FNN) error funtion incoporating the weight decay regularizer. A network with a single hidden-layer and a single output-unit is considered. Explicit vector and matrix canonical forms for the Jacobian and Hessian of the network are presented. Convexity analysis is then performed utilizing the known canonical structure of the Hessian. Next, global optimality characterization of the FNN error function is attempted utilizing the results of convex characterization and a convex monotonic transformation. Based on this global optimality characterization, an iterative algorithm is proposed for global FNN learning. Numerical experiments with benchmark examples show better convergence of our network learning as compared to many existing methods in the literature. The network is also shown to generalize well for a face recognition problem. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Feedforward neural networks

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3D shape reconstruction from multiple silhouettes: Generalization from few views by neural network learning 4th

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4th International workshop on Visual Form, IWVF4 2001

作者： Kumazawa, Itsuo Ohno, Masayoshi Department of Computer Science Tokyo Institute of Technology Ookayama 2-12-1 Meguro-kuTokyo152-8552 Japan

ISBN: (纸本)3540421203

In this report, we present a 3D shape modeling method using the shape’s silhouettes from multiple views to determine the model (polyhedron) parameters. The polyhedron parameters are determined by neural networks, each of which represents the model’s silhouette observed from a view point, and determines the polyhedron parameters by the back propagation algorithm so that the model’s silhouette from each view approximates the corresponding silhouette of the target shape. By conducting basic experiments, we verified the effectiveness of the method. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Geometry

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Encoding nondeterministic finite-state tree automata in sigmoid recursive neural networks

Encoding nondeterministic finite-state tree automata in sigm...

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International Association of pattern recognition, iapr 2000 held jointly with 8th International workshop on Structural and Syntactic pattern recognition, SSPR 2000 and 3rd International workshop on Statistical Techniques in pattern recognition, SPR 2000

作者： Forcada, Mikel L. Carrasco, Rafael C. Departament de Llenguatges i Sistemes Informàtics Universitat d’Alacant AlacantE-03071 Spain

ISBN: (纸本)3540679464

Recently, a number of authors have explored the use of re- cursive recursive neural nets (RNN) for the adaptive processing of trees or tree-like structures. One of the most important language-theoretical formalizations of the processing of tree-structured data is that of finite- state tree automata (FSTA). In many cases, the number of states of a nondeterministic FSTA (NFSTA) recognizing a tree language may be smaller than that of the corresponding deterministic FSTA (DFSTA) (for example, the language of binary trees in which the label of the leftmost k-th order grandchild of the root node is the same as that on the left- most leaf). This paper describes a scheme that directly encodes NFSTA in sigmoid RNN. © Springer-Verlag Berlin Heidelberg 2000.

关键词： neural networks

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Image pattern recognition in natural environment using morphological feature extraction

Image pattern recognition in natural environment using morph...

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作者： Won, Yonggwan Nam, Jiseung Lee, Bae-Ho Department of Computer Engineering Chonnam National University 300 Yongbong-Dong Puk-Gu Kwangju Korea Republic of

ISBN: (纸本)3540679464

The gray-scale morphological Hit-or-Miss transform is theoretically invariant to vertical translation of the input function, which is analogous to gray-value shift of the input images. Designing optimal structuring elements for the Hit-or-Miss transform operator is achieved by neural network learning methodology using a shared-weight neural network (SWNN) architecture. Early stage of the neural network system performs feature extraction using the operator, while the late stage does classification. In experimental studies, this morphological feature-based neural network (MFNN) system is applied to location of human face and automatic recognition of vehicle license plate to examine the property of the operator. The results of the experimental studies show that the gray-scale morphological Hit-or-Miss transform operator is reducing the effects of lighting variation. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Feature extraction

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Biologically inspired architecture of feedforward networks for signal classification

Biologically inspired architecture of feedforward networks f...

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作者： Raudys, Šarūnas Tamošiūnaitė, Minija Institute of Mathematics and Informatics Vytautas Magnus University Vileikos 8 KaunasLT-3035 Lithuania

ISBN: (纸本)3540679464

The hypothesis is that in the lowest hidden layers of biological systems "local subnetworks" are smoothing an input signal. The smoothing accuracy may serve as a feature to feed the subsequent layers of the pattern classification network. The present paper suggests a multistage supervised and "unsupervised" training approach for design and training of multilayer feed-forward networks. Following to the methodology used in the statistical pattern recognition systems we split functionally the decision making process into two stages. In an initial stage, we smooth the input signal in a number of different ways and, in the second stage, we use the smoothing accuracy as a new feature to perform a final classification. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Decision making

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Joint iapr International workshops on Structural, Syntactic pattern recognition and Statistical Techniques in pattern recognition, SSPR 2000 and SPR 2000

Joint IAPR International Workshops on Structural, Syntactic ...

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ISBN: (纸本)3540679464

The proceedings contain 92 papers. The special focus in this conference is on Pierre Devijver Lecture and Hybrid and Combined Methods. The topics include: Some notes on twenty one 21 nearest prototype classifiers;adaptive graphical pattern recognition beyond connectionist-based approaches;current trends in grammatical inference;classifier's complexity control while training multilayer perceptrons;a framework for classifier fusion;image pattern recognition based on examples;a hybrid system for the recognition of hand-written characters;improving statistical measures of feature subsets by conventional and evolutionary approaches;selection of classifiers based on multiple classifier behaviour;the adaptive subspace map for image description and image database retrieval;adaptative automatic target recognition with SVM boosting for outlier detection;a multiresolution causal colour texture model;writer identification;syntactic pattern recognition by error correcting analysis on tree automata;offline recognition of syntax-constrained cursive handwritten text;structural classification for retrospective conversion of documents;segmentation of date field on bank cheques;grammars and discourse theory to describe and recognize mechanical assemblies;computation of the N best parse trees for weighted and stochastic context-free grammars;partitional vs hierarchical clustering using a minimum grammar complexity approach;encoding nondeterministic finite-state tree automata in sigmoid recursive neural networks;a structural matching algorithm using generalized deterministic annealing;alignment and correspondence using singular value decomposition;fast candidate elimination using machine learning techniques and efficient alignment and correspondence using edit distance.

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Reducing the training times of neural classifiers with dataset condensing

Reducing the training times of neural classifiers with datas...

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8th Meeting of the International workshop on Structural and Syntactic pattern recognition, SSPR 2000 and 3rd International workshop on Statistical Techniques in pattern recognition, SPR 2000

作者： Choi, Se-Ho Rockett, Peter Department of Electronic and Electrical Engineering University of Sheffield Mappin Street Sheffield S1 3JD United Kingdom

ISBN: (纸本)3540679464

In this paper we apply a k-nearest-neighbour-based data condensing algorithm to the training sets of multi-layer perceptron neural networks. By removing the overlapping data and retaining only training exemplars adjacent to the decision boundary we are able to significantly speed the network training time while achieving an undegraded misclassification rate compared to a network trained on the unedited training set. We report results on a range of synthetic and real datasets which indicate that a speed-up of an order of magnitude in the network training time is typical. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Network layers

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Multiple comparison procedures for determining the optimal complexity of a model

Multiple comparison procedures for determining the optimal c...

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8th Meeting of the International workshop on Structural and Syntactic pattern recognition, SSPR 2000 and 3rd International workshop on Statistical Techniques in pattern recognition, SPR 2000

作者： Galindo, Pedro L. Pizarro-Junquera, Joaquín Guerrero, Elisa Spain

ISBN: (纸本)3540679464

We aim to determine which of a set of competing models is statistically best, that is, on average. A way to define "on average" is to consider the performance of these algorithms averaged over all the training sets that might be drawn from the underlying distribution. When comparing more than two means, an ANOVA F-test tells you whether the means are significantly different, but it does not tell you which means differ from each other. A simple approach is to test each possible difference by a paired t-test. However, the probability of making at least one type I error increases with the number of tests made. Multiple comparison procedures provide different solutions. We discuss these techniques and apply the well known Bonferroni method in order to determine the optimal degree in polynomial fitting and the optimal number of hidden neurons in feedforward neural networks. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Feedforward neural networks

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On-line handwritten character recognition by a hybrid method based on neural networks and pattern matching 3rd

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3rd International workshop on artificial neural networks, IWANN 1995

作者： Cho, Jung-Wook Lee, Soo-Young Park, Cheol Hoon Computation and Neural Systems Laboratory Department of Electrical Engineering Korea Advanced Institute of Science and Technology 373-1 Kusung-Dong Yusung-Gu Taejon305-701 Korea Republic of

ISBN: (纸本)3540594973

A hybrid system is developed for on-line recognition of hand-written Korean characters. Each syllable consists of several Korean alphabets and is written in a rectangular box to result in a 2-dimensional compositions of alphabets. Although only 24 alphabets exist, the number of syllables easily exceeds 3000. Therefore, instead of the syllables, the 24 alphabets are used as basic recognition unit, which causes difficult segmentation and recognition problems. Each Korean alphabet has at most 4 strokes, and 4 neural networks are trained separately for alphabets with different number of strokes. To improve the recognition preformance, after neural network classifications, classical pattern matching is also applied to check validity of the decisions made by the neural network classifiers. The hybrid systems is robust on distortion and rotation of the characters. © 1995, Springer Verlag. All rights reserved.

关键词： Hybrid systems

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Recurrent fuzzy logic using neural network 3rd

Recurrent fuzzy logic using neural network

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3rd World Wisepersons workshop on Fuzzy Logic and neural networks/Genetic Algorithms, WWW 1994

作者： Khan, Emdad Unal, Fatih Intelligent Systems Group Embedded Systems Division National Semiconductor Santa ClaraCA95052 United States

ISBN: (纸本)9783540606079

In this paper, a novel method is presented to combine neural nets with fuzzy logic. The combined technology is based on modified NeuFuz ([I], [2],[3]) using recurrent neural networks. The recurrent information of neural net is directly mapped to a new type offuzzy logic, called "recurrent"fuzzy logic. Recurrencypreserves temporal information and yields superior performance for context dependent applications. It also reduces the convergence time I Simulations show good improvements in accuracy and speed of convergence in pattern recognition applications. © Springer-Verlag Berlin Heidelberg 1995.

关键词： Recurrent neural networks

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