一种新的特征提取方法及其在模式识别中的应用

核典型相关分析(KCCA)是一种有监督的机器学习方法,可以有效地提取非线性特征。然而随着训练样本数目的增加,标准的KCCA方法的计算复杂度会随之增加。针对此缺点,提出一种改进的KCCA方法：首先用几何特征选择方法选择一个训练样本子集并将其映射到再生核希尔伯特空间(RKHS),然后设计了一种提升特征提取效率的算法,该算法按照对特征分类贡献的大小巧妙地选取样本的特征值,进而求出其相应的特征向量,最后将改进的KCCA与支持向量数据描述(SVDD)多分类器相结合用于分类识别。在ORL人脸图像数据库上的实验结果表明,改进的方法相对传统的KCCA方法,在不影响识别率的情况下提高了人脸识别速度,减小了系统存储量。     

A fuzzy neural network and its application to pattern recognition

Defines four types of fuzzy neurons and proposes the structure of a four-layer feedforward fuzzy neural network (FNN) and its associated learning algorithm. The proposed four-layer FNN performs well when used to recognize shifted and distorted training patterns. When an input pattern is provided, the network first fuzzifies this pattern and then computes the similarities of this pattern to all of the learned patterns. The network then reaches a conclusion by selecting the learned pattern with the highest similarity and gives a nonfuzzy output. The 26 English alphabets and the 10 Arabic numerals, each represented by 16×16 pixels, were used as original training patterns. In the simulation experiments, the original 36 exemplar patterns were shifted in eight directions by 1 pixel (6.25% to 8.84%) and 2 pixels (12.5% to 17.68%). After the FNN has been trained by the 36 exemplar patterns, the FNN can recall all of the learned patterns with 100% recognition rate. It can also recognize patterns shifted by 1 pixel in eight directions with 100% recognition rate and patterns shifted by 2 pixels in eight directions with an average recognition rate of 92.01%. After the FNN has been trained by the 36 exemplar patterns and 72 shifted patterns, it can recognize patterns shifted by 1 pixel with 100% recognition rate and patterns shifted by 2 pixels with an average recognition rate of 98.61%. The authors have also tested the FNN with 10 kinds of distorted patterns for each of the 36 exemplars. The FNN can recognize all of the distorted patterns with 100% recognition rate. The proposed FNN can also be adapted for applications in some other pattern recognition problems     

On the Mitchell similarity measure and its application to pattern recognition

This paper is a response to the similarity measure and pattern recognition problem of Mitchell that was published in Pattern Recognition Letters, 2003. The purpose of this paper is threefold. First, we reviewed and revised her computation for similarity measures. Second, we proved that the similarity values for the one-norm should be larger than that for the two-norm for her pattern recognition problem. Third, we proposed a more scattered similarity measure to help researchers determine patterns. Our findings may shed light on the ongoing debate between Li and Cheng, 2002, Mitchell, 2003.     

A multiresolution wavelet networks architecture and its application to pattern recognition

This paper aims at addressing a challenging research in both fields of the wavelet neural network theory and the pattern recognition. A novel architecture of the wavelet network based on the multiresolution analysis (MRWN) and a novel learning algorithm founded on the Fast Wavelet Transform (FWTLA) are proposed. FWTLA has numerous positive sides compared to the already existing algorithms. By exploiting this algorithm to learn the MRWN, we suggest a pattern recognition system (FWNPR). We show firstly its classification efficiency on many known benchmarks and then in many applications in the field of the pattern recognition. Extensive empirical experiments are performed to compare the proposed methods with other approaches.     

指纹拓扑模式构建及其在识别中的应用

现有指纹识别算法对指纹图像质量要求较高,特别是在变形指纹识别中鲁棒性不强。基于指纹特征属性和细节点、脊线拓扑关系的拓扑模式从整体到局部,具备以拓扑性质为基础的不同层次的几何不变性,在指纹变形中具有更高的鲁棒性。通过对指纹细节点、脊线属性及点、线拓扑关系的分析与计算,构建了两种基于指纹细节点、脊线的拓扑模式,实验结果验证了所构建的两种拓扑模式在指纹平移、旋转、缩放及轻微非线性变形中具有较高的鲁棒性,可用于变形等低质量指纹图像的识别中     

一种新的GBPA生成方法及其在模式识别中的应用

广义证据理论是一种在不完备识别框架中处理多传感器信息融合问题的实用方法.由于时代环境的影响,人们的认知存在局限性,难免会将不完备的识别框架认为是完备的,经典证据理论在这种情况下并不完全适用.因此,根据广义证据理论提出一种新的广义基本概率赋值(generalized basic probability assignment,GBPA)生成方法.该方法首先根据训练数据分别构造样本类别和测试样本的广义三角模糊数模型;然后通过计算样本和类别间的广义三角模糊距离生成GBPA;最后使用广义组合规则融合所有证据并得出最终的结论.Iris数据集的实验结果表明所提方法合理有效,即使在样本不足的情况下仍有较高的分类精度.     

A concept of observed weighted entropy and its application to pattern recognition

The concept of weighted entropy is generalized in the form of ‘observed weighted entropy’ to account for some defects in the observation process, and then the latter is combined with the maximum entropy principle to derive selection criteria which could be applied in pattern recognition systems.     

基于遗传算法的模糊模式识别及其应用

针对谷物害虫图像识别的特点，提出了基于模糊理论的害虫图像识别方法。对模糊C-均值聚类做了简要分析和评论，在此基础上将遗传算法引入模糊聚类，利用其搜索的随机和并行性，克服了模糊C-均值聚类的局部性和对初始聚类中心的敏感性；采用了基于贴近度和择近原则的模糊识别方法，分析了格贴近度的不足之处，并对之进行了改进。实验结果表明了上述方法是有效的，可行的，扩大了遗传算法和模糊理论的应用范围。     

On the Dengfeng–Chuntian similarity measure and its application to pattern recognition

Recently Dengfeng and Chuntian defined the first operational definition of a similarity measure for intuitionistic fuzzy sets and showed how it may be used in pattern recognition problems. Unfortunately the Dengfeng and Chuntian operator may give counter-intuitive results. We show how a simple modification of the Dengfeng–Chuntian operator may correct this problem. We illustrate the application of the modified similarity measure on a simple pattern recognition problem.     

Dynamical pattern recognition for univariate time series and its application to an axial compressor

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.     

Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition

The polynomial classifier (PC) that takes the binomial terms of reduced subspace features as inputs has shown superior performance to multilayer neural networks in pattern classification. In this paper, we propose a class-specific feature polynomial classifier (CFPC) that extracts class-specific features from class-specific subspaces, unlike the ordinary PC that uses a class-independent subspace. The CFPC can be viewed as a hybrid of ordinary PC and projection distance method. The class-specific features better separate one class from the others, and the incorporation of class-specific projection distance further improves the separability. The connecting weights of CFPC are efficiently learned class-by-class to minimize the mean square error on training samples. To justify the promise of CFPC, we have conducted experiments of handwritten digit recognition and numeral string recognition on the NIST Special Database 19 (SD19). The digit recognition task was also benchmarked on two standard databases USPS and MNIST. The results show that the performance of CFPC is superior to that of ordinary PC, and is competitive with support vector classifiers (SVCs).     

基于模式识别的正交试验数据分析及其应用

针对现有正交试验数据分析方法有操作复杂和适应性弱的弊端,提出基于模式识别的最优判别平面(ODP)分析法。该法首先按Fisher准则而建立2个相互垂直的矢量,再将试验数据投影到这2个正交轴上,从而得到二维特征矢量,据此确定各因素对指标的影响程度。分析SIS热熔压敏胶性能试验数据结果,ODP分析法的结果与实际相符,且不受正交表指标数和有无空白列的限制,是一种简单而有效的正交试验数据分析方法,可在冶金化工领域推广。     

The build of a dynamic classifier selection ICBP system and its application to pattern recognition

Abstract  

In neural network ensemble, the diversity of its constitutive component networks is a crucial factor to boost its generalization performance. In terms of how each ensemble system solves the problem, we can roughly categorize the existing ensemble mechanism into two groups: data-driven and model-driven ensembles. The former engenders diversity to ensemble members by manipulating the data, while the latter realizes ensemble diversity by manipulating the component models themselves. Within a neural network ensemble, standard back-propagation (BP) networks are usually used as a base component. However, in this article, we will use our previously designed improved circular back-propagation (ICBP) neural network to establish such an ensemble. ICBP differentiates from BP network not only because an extra anisotropic input node is added, but also more importantly, because of the introduction of the extra node, it possesses an interesting property apart from the BP network, i.e., just through directly assigning different sets of values 1 and −1 to the weights connecting the extra node to all the hidden nodes, we can construct a set of heterogeneous ICBP networks with different hidden layer activation functions, among which we select four typical heterogeneous ICBPs to build a dynamic classifier selection ICBP system (DCS-ICBP). The system falls into the category of model-driven ensemble. The aim of this article is to explore the relationship between the explicitly constructed ensemble and the diversity scale, and further to verify feasibility and effectiveness of the system on classification problems through empirical study. Experimental results on seven benchmark classification tasks show that our DCS-ICBP outperforms each individual ICBP classifier and surpasses the performance of combination of ICBP using the majority voting technique, i.e. majority voting ICBP system (MVICBP). The successful simulation results validate that in DCS-ICBP we provide a new constructive method for diversity enforcement for ICBP ensemble systems.     

A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition problems

A novel distance measure between two intuitionistic fuzzy sets (IFSs) is proposed in this paper. The introduced measure formulates the information of each set in matrix structure, where matrix norms in conjunction with fuzzy implications can be applied to measure the distance between the IFSs. The advantage of this novel distance measure is its flexibility, which permits different fuzzy implications to be incorporated by extending its applicability to several applications where the most appropriate implication is used. Moreover, the proposed distance might be expressed equivalently by using either intuitionistic fuzzy sets or interval‐valued fuzzy sets. Appropriate experimental configurations have taken place to compare the proposed distance measure with similar distance measures from the literature, by applying them to several pattern recognition problems. The results are very promising because the performance of the new distance measure outperforms the corresponding performance of well‐known IFSs measures, by recognizing the patterns correctly and with high degree of confidence. © 2012 Wiley Periodicals, Inc.     

Parallel-hierarchical network learning methods and their application to pattern recognition

This paper considers provisions necessary for developing parallel-hierarchical network learning methods underlain by the idea of population coding in an artificial neural network and its approximation to natural neural networks. Mathematical parallel-hierarchical network learning models and a combined parallel-hierarchical network learning method are developed for recognizing static and dynamic patterns.     

An application of stochastic languages to fingerprint pattern recognition

The purpose of this paper is to present a stochastic syntactic approach for representation and classification of fingerprint patterns.The fingerprint impressions are subdivided into sampling squares which are preprocessed for feature extraction. First, a brief summary of application of a class of context-free languages for recognition of fingerprints is presented. Next, using the same set of features, a class of stochastic context-free languages was used to further classify the fingerprint impressions. The recognizers using the class of context-free and stochastic context-free languages are named the first-level and second-level classifiers, respectively. Experimental results in terms of real data fingerprints are presented.     

A modified hybrid neural network for pattern recognition and its application to SSW complex in EEG

In this study, a modified hybrid neural network with asymmetric basis functions is presented for feature extraction of spike and slow wave complexes in electroencephalography (EEG). Feature extraction process has a great importance in all pattern recognition and classification problems. A gradient descent algorithm, indeed a back propagation type, is adapted to the proposed artificial neural network. The performance of the proposed network is measured using a support vector machine classifier fed by features extracted using the proposed neural network. The results show that the proposed neural network model can effectively be used in pattern recognition tasks. In experiments, real EEG data are used.     

Optimal pattern recognition procedures and their application

Results on Bayesian classification procedures, optimal on structures such as Markov chain and independent features, are reviewed. Numerical results of predicting protein secondary structure based on Bayesian classification procedures on non-stationary Markov chains are discussed. Complementarity relations for encoding bases in one DNA strand are presented. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 41–54, November–December 2007.