An investigation of the modified direction feature for cursive character recognition

This paper describes and analyses the performance of a novel feature extraction technique for the recognition of segmented/cursive characters that may be used in the context of a segmentation-based handwritten word recognition system. The modified direction feature (MDF) extraction technique builds upon the direction feature (DF) technique proposed previously that extracts direction information from the structure of character contours. This principal was extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image.In order to improve on the DF extraction technique, a number of modifications were undertaken. With a view to describe the character contour more effectively, a re-design of the direction number determination technique was performed. Also, an additional global feature was introduced to improve the recognition accuracy for those characters that were most frequently confused with patterns of similar appearance. MDF was tested using a neural network-based classifier and compared to the DF and transition feature (TF) extraction techniques. MDF outperformed both DF and TF techniques using a benchmark dataset and compared favourably with the top results in the literature. A recognition accuracy of above 89% is reported on characters from the CEDAR dataset.     

Classification of acoustic events using SVM-based clustering schemes

Acoustic events produced in controlled environments may carry information useful for perceptually aware interfaces. In this paper we focus on the problem of classifying 16 types of meeting-room acoustic events. First of all, we have defined the events and gathered a sound database. Then, several classifiers based on support vector machines (SVM) are developed using confusion matrix based clustering schemes to deal with the multi-class problem. Also, several sets of acoustic features are defined and used in the classification tests. In the experiments, the developed SVM-based classifiers are compared with an already reported binary tree scheme and with their correlative Gaussian mixture model (GMM) classifiers. The best results are obtained with a tree SVM-based classifier that may use a different feature set at each node. With it, a 31.5% relative average error reduction is obtained with respect to the best result from a conventional binary tree scheme.     

A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests

In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm.The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis.     

一种新型的多元分类支持向量机

最小二乘支持向量机采用最小二乘线性系统代替传统的支持向量机采用二次规划方法解决模式识别问题。该文详细推理和分析了二元分类最小二乘支持向量机算法，构建了多元分类最小二乘支持向量机，并通过典型样本进行测试，结果表明采用多元分类最小二乘支持向量机进行模式识别是有效、可行的。     

An intelligent character recognizer for Telugu scripts using multiresolution analysis and associative memory

The present work is an attempt to develop a robust character recognizer for Telugu texts. We aim at designing a recognizer, which exploits the inherent characteristics of the Telugu Script. Our proposed method uses wavelet multi-resolution analysis for the purpose extracting features and associative memory model to accomplish the recognition tasks. Our system learns the style and font from the document itself and then it recognizes the remaining characters in the document. The major contribution of the present study can be outlined as follows. It is a robust OCR system for Telugu printed text. It avoids feature extraction process and it exploits the inherent characteristics of the Telugu character by a clever selection of Wavelet Basis function, which extracts the invariant features of the characters. It has a Hopfield-based Dynamic Neural Network for the purpose of learning and recognition. This is important because it overcomes the inherent difficulties of memory limitation and spurious states in the Hopfield Network. The DNN has been demonstrated to be efficient for associative memory recall. However, though it is normally not suitable for image processing application, the multi-resolution analysis reduces the sizes of the images to make the DNN applicable to the present domain. Our experimental results show extremely promising results.     

SVM-based feature extraction for face recognition

The primary goal of linear discriminant analysis (LDA) in face feature extraction is to find an effective subspace for identity discrimination. The introduction of kernel trick has extended the LDA to nonlinear decision hypersurface. However, there remained inherent limitations for the nonlinear LDA to deal with physical applications under complex environmental factors. These limitations include the use of a common covariance function among each class, and the limited dimensionality inherent to the definition of the between-class scatter. Since these problems are inherently caused by the definition of the Fisher's criterion itself, they may not be solvable under the conventional LDA framework. This paper proposes to adopt a margin-based between-class scatter and a regularization process to resolve the issue. Essentially, we redesign the between-class scatter matrix based on the SVM margins to facilitate an effective and reliable feature extraction. This is followed by a regularization of the within-class scatter matrix. Extensive empirical experiments are performed to compare the proposed method with several other variants of the LDA method using the FERET, AR, and CMU-PIE databases.     

SVM-based posture identification with a single waist-located triaxial accelerometer

Analysis of human body movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease o stroke patients, it is crucial to monitor and assess their daily life activities. The main goal of this work is the characterization of basic activities using a single triaxial accelerometer located at the waist. This paper presents a novel postural detection algorithm based in SVM methods which is able to detect and identify Walking, Stand, Sit, Lying, Sit to Stand, Stand to sit, Bending up/down, Lying from Sit and Sit from Lying transitions with a sensitivity of 97% and specificity of 84% with 2884 postures analyzed from 31 healthy volunteers. Parameters and models found have been tested in another dataset from Parkinson’s disease patients, achieving results of 98% of sensitivity and 78% of specificity in postural transitions. The proposed algorithm has been optimized to be easily implemented in real-time system for on-line monitoring applications.     

基于多人决策理论的船舶辐射噪声识别

船舶辐射噪声的识别非常复杂,为提高其分类器的分类性能和可靠性．提出一种基于多人决策理论的多分类器决策模型和算法．通过采用Welch谱、线性预测编码谱和Burg谱3种特征对应的BP神经网络和支持向量机分类器组成的6个分类结果进行群体决策,并对海上实测的3类目标辐射噪声数据进行分类．实验结果表明,对3类目标的总体正确识别概率达到96．29％．比单个分类器具有更好的分类性能．     

Cycle-breaking acceleration for support vector regression

Support vector regression (SVR) is a powerful tool in modeling and prediction tasks with widespread application in many areas. The most representative algorithms to train SVR models are Shevade et al.'s Modification 2 and Lin's WSS1 and WSS2 methods in the LIBSVM library. Both are variants of standard SMO in which the updating pairs selected are those that most violate the Karush-Kuhn-Tucker optimality conditions, to which LIBSVM adds a heuristic to improve the decrease in the objective function. In this paper, and after presenting a simple derivation of the updating procedure based on a greedy maximization of the gain in the objective function, we show how cycle-breaking techniques that accelerate the convergence of support vector machines (SVM) in classification can also be applied under this framework, resulting in significantly improved training times for SVR.     

Estimation of stability lobe diagrams in milling with continuous learning algorithms

The productivity of milling processes is limited by the occurrence of chatter vibrations. The correlation of the maximum stable cutting depth and the spindle speed can be shown in a stability lobe diagram (SLD). The stability is different for different width of cut and can change with the axis positions. Today it is already a great effort to estimate the SLD only for one position. Many experiments are necessary to measure the SLD or derive a detailed mathematical model to calculate the SLD. Moreover not only the cutting depth, but also the cutting width should be represented in the SLD. This paper presents a new approach to assess the process stability based on measured acceleration signals. The multidimensional stability lobe diagram (MSLD) are derived during the production using two new continuously learning algorithms. In this paper the application of a continuous learning support vector machine and a continuous neural network is shown. The support vector machine and the neural network are extended to make them capable for continuous learning and time-variant systems. A new trust criterion is introduced, which gives information about the prediction quality of the output for the selected input region. The learned MSLDs are evaluated against analytically calculated MSLDs and the learning algorithms can reproduce the analytical results very well.     

基于支持向量机的嵌入式软仪表

过去几年中，神经网络尤其是径向基网络得到广泛了应用，支持向量机作为一种新兴的学习机和神经网络相比有着极大的优越性，以某炼油厂催化裂化单元中的轻柴油凝固点为例，从Matlab算法验证和工程实践两方面说明了支持向量机在软测量建模中的巨大优越性。     

SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary criterion

An efficient filter feature selection (FS) method is proposed in this paper, the SVM-FuzCoC approach, achieving a satisfactory trade-off between classification accuracy and dimensionality reduction. Additionally, the method has reasonably low computational requirements, even in high-dimensional feature spaces. To assess the quality of features, we introduce a local fuzzy evaluation measure with respect to patterns that embraces fuzzy membership degrees of every pattern in their classes. Accordingly, the above measure reveals the adequacy of data coverage provided by each feature. The required membership grades are determined via a novel fuzzy output kernel-based support vector machine, applied on single features. Based on a fuzzy complementary criterion (FuzCoC), the FS procedure iteratively selects features with maximum additional contribution in regard to the information content provided by previously selected features. This search strategy leads to small subsets of powerful and complementary features, alleviating the feature redundancy problem. We also devise different SVM-FuzCoC variants by employing seven other methods to derive fuzzy degrees from SVM outputs, based on probabilistic or fuzzy criteria. Our method is compared with a set of existing FS methods, in terms of performance capability, dimensionality reduction, and computational speed, via a comprehensive experimental setup, including synthetic and real-world datasets.     

SVM-based hierarchical architectures for handwritten Bangla character recognition

We propose support vector machine (SVM) based hierarchical classification schemes for recognition of handwritten Bangla characters. A comparative study is made among multilayer perceptron, radial basis function network and SVM classifier for this 45 class recognition problem. SVM classifier is found to outperform the other classifiers. A fusion scheme using the three classifiers is proposed which is marginally better than SVM classifier. It is observed that there are groups of characters having similar shapes. These groups are determined in two different ways on the basis of the confusion matrix obtained from SVM classifier. In the former, the groups are disjoint while they are overlapped in the latter. Another grouping scheme is proposed based on the confusion matrix obtained from neural gas algorithm. Groups are disjoint here. Three different two-stage hierarchical learning architectures (HLAs) are proposed using the three grouping schemes. An unknown character image is classified into a group in the first stage. The second stage recognizes the class within this group. Performances of the HLA schemes are found to be better than single stage classification schemes. The HLA scheme with overlapped groups outperforms the other two HLA schemes.     

Building a compact online MRF recognizer for large character set by structured dictionary representation and vector quantization technique

This paper describes a method for building a compact online Markov random field (MRF) recognizer for large handwritten Japanese character set using structured dictionary representation and vector quantization (VQ) technique. The method splits character patterns into radicals, whose models by MRF are shared by different character classes such that a character model is constructed from the constituent radical models. Many distinct radicals are shared by many character classes with the result that the storage space of model dictionary can be saved. Moreover, in order to further compress the parameters, VQ technique to cluster parameter sequences of the mean vectors and covariance matrixes for MRF unary features and binary features as well as the transition probabilities of each state into groups was employed. By sharing a common parameter sequence for each group, the dictionary of the MRF recognizer can be greatly compressed without recognition accuracy loss.     

文字相关的单字符字体识别方法

汉字具有丰富的字体类型,并且不同的字体在汉字结构上有显著的不同,现在的OCR技术侧重字的识别,而对字体识别的关注较少。提出文字相关的单字符字体识别方法,利用文字相关的先验信息及字体结构特征,对字体的相似性度量采用向量空间模型,并针对常用66款简体字进行实验,得到了较好的平均识别率。     

A hybrid SVM based decision tree

We have proposed a hybrid SVM based decision tree to speedup SVMs in its testing phase for binary classification tasks. While most existing methods addressed towards this task aim at reducing the number of support vectors, we have focused on reducing the number of test datapoints that need SVM’s help in getting classified. The central idea is to approximate the decision boundary of SVM using decision trees. The resulting tree is a hybrid tree in the sense that it has both univariate and multivariate (SVM) nodes. The hybrid tree takes SVM’s help only in classifying crucial datapoints lying near decision boundary; remaining less crucial datapoints are classified by fast univariate nodes. The classification accuracy of the hybrid tree is guaranteed by tuning a threshold parameter. Extensive computational comparisons on 19 publicly available datasets indicate that the proposed method achieves significant speedup when compared to SVMs, without any compromise in classification accuracy.