一种改进的级联AdaBoost分类器

在基于级联结构的AdaBoost分类器中引入了辅助判决函数。当样本被某级AdaBoost分类器判为假后,使用该级的辅助判决函数对其进行二次判决。辅助判决函数不仅考虑了当前级分类器的判决结果,还考虑前面分类器的历史判决信息。实验结果表明,该方法相对级联AdaBoost分类器以及二维级联AdaBoost具有更好的识别效果。     

A parallel incremental extreme SVM classifier

The classification algorithm extreme SVM (ESVM) proposed recently has been proved to provide very good generalization performance in relatively short time, however, it is inappropriate to deal with large-scale data set due to the highly intensive computation. Thus we propose to implement an efficient parallel ESVM (PESVM) based on the current and powerful parallel programming framework MapReduce. Furthermore, we investigate that for some new coming training data, it is brutal for ESVM to always retrain a new model on all training data (including old and new coming data). Along this line, we develop an incremental learning algorithm for ESVM (IESVM), which can meet the requirement of online learning to update the existing model. Following that we also provide the parallel version of IESVM (PIESVM), which can solve both the large-scale problem and the online problem at the same time. The experimental results show that the proposed parallel algorithms not only can tackle large-scale data set, but also scale well in terms of the evaluation metrics of speedup, sizeup and scaleup. It is also worth to mention that PESVM, IESVM and PIESVM are much more efficient than ESVM, while the same solutions as ESVM are exactly obtained.     

结合本征分解和抽样学习的快速SVM分类器

为了提高经典支持向量机(SVM)分类器的运算效率,提出一种改进的快速SVM分类器。针对低维空间向高维空间变换时核矩阵运算效率低的问题,采用本征分解方法对核矩阵进行降维处理,得到核矩阵的近似表示;同时,对训练子集进行划分,随机抽样训练样本进行学习,进一步提高SVM分类器的运算效率。图像分类实验结果表明,改进的SVM分类器不仅分类正确率高于经典SVM、随机森林和神经网络分类器,而且训练耗时最少,平均分类耗时也低于经典SVM分类器。     

A novel multiplex cascade classifier for pedestrian detection

Reliable pedestrian detection is of great importance in visual surveillance. In this paper, we propose a novel multiplex classifier model, which is composed of two multiplex cascades parts: Haar-like cascade classifier and shapelet cascade classifier. The Haar-like cascade classifier filters out most of irrelevant image background, while the shapelet cascade classifier detects intensively head-shoulder features. The weighted linear regression model is introduced to train its weak classifiers. We also introduce a structure table to label the foreground pixels by means of background differences. The experimental results illustrate that our classifier model provides satisfying detection accuracy. In particular, our detection approach can also perform well for low resolution and relatively complicated backgrounds.     

Construction of a classifier using AdaBoost for information filtering

We describe an information filtering system using AdaBoost. To realize the filtering system, we created a user profile which presents the users interests. Since the users interests are complex, the user profile becomes a nonlinear discriminant function. However, it is difficult to decide on an appropriate discriminant function. We used AdaBoost to modify the appropriate user profile. AdaBoost is an ensemble algorithm which combines weak learners and improves the accuracy of a classifier. In this method, the weak learners for AdaBoost is a linear discriminant function which is created with a genetic algorithm. We carried out experiments for an information filtering service on an NTCIR2 test collection, and we discuss the effectiveness of the method.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Fingerprint segmentation based on an AdaBoost classifier

Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system (AFIS). Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and the most importantly, exclude many spurious minutiae located at the boundary of foreground. In this paper, a new fingerprint segmentation algorithm is presented. First, two new features, block entropy and block gradient entropy, are proposed. Then, an AdaBoost classifier is designed to discriminate between foreground and background blocks based on these two features and five other commonly used features. The classification error rate (Err) and McNemar’s test are used to evaluate the performance of our method. Experimental results on FVC2000, FVC2002 and FVC2004 show that our method outperforms other methods proposed in the literature both in accuracy and stability.     

基于SVM和AdaBoost的人脸检测算法

提出了一种结合SVM和AdaBoost的人脸检测算法。该算法首先根据训练样本中的人脸和非人脸区域训练作为分量分类器的支持向量机（SVM）,然后通过AdaBoost算法把这些训练好的分量分类器组合成一个总体分类器;再利用该总体分类器来区分测试样本中的人脸和非人脸区域。实验结果表明,该算法不但提高了识别的正确率,而且大大提高了检测速度。     

文本分类中基于AdaBoost.MR的改进中心法

文本信息的爆炸式增长提出了对适宜实时应用的简单快速文本分类的需求,中心分类法虽然快速,但它所基于的假设常常与事实相违,导致分类模型偏差.基于集成学习中的AdaBoost.MR算法,通过利用其自适应维护权重分布的特点,用每轮的权重分布,修正传统中心法分类器偏差,突出被误分类的文档的影响,降低其误分类概率.在YQ-WEBBENCH-V1.1上的实验表明效果良好.     

基于多特征的打印文件层级分类研究

打印文件鉴别是一种广泛应用于安全领域的取证技术,因此对其检测的准确率和速度均有较高要求。考虑到单个特征的信息不全,基于多特征融合的方法来提高准确率,同时使用基于Ada Boost的SVM级联分类器进行分类判定。研究过程包括数据采集、图像预处理、GMM和LBP特征提取、特征融合、单个SVM分类器分类以及基于Ada Boost的层级SVM分类器分类。通过对4 000张图片集提取GMM和LBP特征,然后进行特征融合,输入分类器分类,结果表明,该方法能够在一定程度上提高鉴别的准确率和速度,具有良好的可扩展性。     

Road intersection detection and classification using hierarchical SVM classifier

In this paper, a hierarchical multi-classification approach using support vector machines (SVM) has been proposed for road intersection detection and classification. Our method has two main steps. The first involves the road detection. For this purpose, an edge-based approach has been developed using the bird’s eye view image which is mapped from the perspective view of the road scene. Then, the concept of vertical spoke has been introduced for road boundary form extraction. The second step deals with the problem of road intersection detection and classification. It consists on building a hierarchical SVM classifier of the extracted road forms using the unbalanced decision tree architecture. Many measures are incorporated for good evaluation of the proposed solution. The obtained results are compared to those of Choi et al. (2007).     

A novel cascade ensemble classifier system with a high recognition performance on handwritten digits

This paper presents a novel cascade ensemble classifier system for the recognition of handwritten digits. This new system aims at attaining a very high recognition rate and a very high reliability at the same time, in other words, achieving an excellent recognition performance of handwritten digits. The trade-offs among recognition, error, and rejection rates of the new recognition system are analyzed. Three solutions are proposed: (i) extracting more discriminative features to attain a high recognition rate, (ii) using ensemble classifiers to suppress the error rate and (iii) employing a novel cascade system to enhance the recognition rate and to reduce the rejection rate. Based on these strategies, seven sets of discriminative features and three sets of random hybrid features are extracted and used in the different layers of the cascade recognition system. The novel gating networks (GNs) are used to congregate the confidence values of three parallel artificial neural networks (ANNs) classifiers. The weights of the GNs are trained by the genetic algorithms (GAs) to achieve the overall optimal performance. Experiments conducted on the MNIST handwritten numeral database are shown with encouraging results: a high reliability of 99.96% with minimal rejection, or a 99.59% correct recognition rate without rejection in the last cascade layer.     

基于混沌粒子群优化的SVM分类器研究

支持向量机(SVM)分类器能较好地解决小样本、非线性、高维等分类问题,具有很强的实用性。然而,支持向量机训练参数的选择对其分类精度有着很大的影响。常用的支持向量机优化方法有遗传算法、粒子群算法都存在易陷入局部极值,优化效果较差的不足。为解决上述问题在粒子群优化算法中引入混沌思想,提出了基于混沌粒子群优化算法(CPSO)的SVM分类器优化方法,CPSO算法能提高种群的多样性和粒子搜索的遍历性,从而有效地提高了PSO算法的收敛速度和精度,更好的优化SVM分类器。并以网络异常入侵检测为研究对象进行仿真,实验结果表明,根据混沌粒子群优化的SVM分类器比传统算法优化的SVM分类器的精度高,速度快。     

A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications

Several methods utilizing common spatial pattern (CSP) algorithm have been presented for improving the identification of imagery movement patterns for brain computer interface applications. The present study focuses on improving a CSP-based algorithm for detecting the motor imagery movement patterns. A discriminative filter bank of CSP method using a discriminative sensitive learning vector quantization (DFBCSP-DSLVQ) system is implemented. Four algorithms are then combined to form three methods for improving the efficiency of the DFBCSP-DSLVQ method, namely the kernel linear discriminant analysis (KLDA), the kernel principal component analysis (KPCA), the soft margin support vector machine (SSVM) classifier and the generalized radial bases functions (GRBF) kernel. The GRBF is used as a kernel for the KLDA, the KPCA feature selection algorithms and the SSVM classifier. In addition, three types of classifiers, namely K-nearest neighbor (K-NN), neural network (NN) and traditional support vector machine (SVM), are employed to evaluate the efficiency of the classifiers. Results show that the best algorithm is the combination of the DFBCSP-DSLVQ method using the SSVM classifier with GRBF kernel (SSVM-GRBF), in which the best average accuracy, attained are 92.70% and 83.21%, respectively. Results of the Repeated Measures ANOVA shows the statistically significant dominance of this method at p <?0.05. The presented algorithms are then compared with the base algorithm of this study i.e. the DFBCSP-DSLVQ with the SVM-RBF classifier. It is concluded that the algorithms, which are based on the SSVM-GRBF classifier and the KLDA with the SSVM-GRBF classifiers give sufficient accuracy and reliable results.

     

Identification of EMG signals using discriminant analysis and SVM classifier

The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. In this paper, a classification technique is proposed to classify signals required for a prosperous arm prosthesis control by using surface EMG signals. This work uses recorded EMG signals generated by biceps and triceps muscles for four different movements. Each signal has one single pattern and it is essential to separate and classify these patterns properly. Discriminant analysis and support vector machine (SVM) classifier have been used to classify four different arm movement signals. Prior to classification, proper feature vectors are derived from the signal. The feature vectors are generated by using mean absolute value (MAV). These feature vectors are provided as inputs to the identification/classification system. Discriminant analysis using five different approaches, classification accuracy rates achieved from very good (98%) to poor (96%) by using 10-fold cross validation. SVM classifier gives a very good average accuracy rate (99%) for four movements with the classification error rate 1%. Correct classification rates of the applied techniques are very high which can be used to classify EMG signals for prosperous arm prosthesis control studies.     

Neighborhood rough set and SVM based hybrid credit scoring classifier

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score according to the applicant’s input features: (1) using neighborhood rough set to select input features; (2) using grid search to optimize RBF kernel parameters; (3) using the hybrid optimal input features and model parameters to solve the credit scoring problem with 10-fold cross validation; (4) comparing the accuracy of the proposed method with other methods. Experiment results demonstrate that the neighborhood rough set and SVM based hybrid classifier has the best credit scoring capability compared with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.     

A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier

Based on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct a multi-class classifier. Different coupling strategies to reconstruct a multi-class classifier from binary SVM classifiers are compared with application to fault diagnosis of transmission line. Majority voting, a mixture matrix and self-organizing map (SOM) network are compared in reconstructing the global classification decision. In order to evaluate the method’s efficiency, one-against-all, decision directed acyclic graph (DDAG) and decision-tree (DT) algorithm based SVM are compared too. The comparison is done with simulations and the best method is validated with experimental data.     

A self-constructing fuzzy CMAC model and its applications

This work presents a self-constructing fuzzy cerebellar model articulation controller (SC-FCMAC) model for various applications. A self-constructing learning algorithm, which consists of the self-clustering method (SCM) and the back-propagation algorithm, is presented. The proposed SCM scheme is a rapid, one-pass algorithm which dynamically estimates the number of hypercube cells in input data space. The clustering method does not require prior knowledge, such as the number of clusters in a data set. The back-propagation algorithm is applied to tune the adjustable parameters. Simulation results are obtained to show the performance and applicability of the proposed model.     

Context and profile based cascade classifier for efficient people detection and safety care system

This study propose a system of extracting and tracking objects for a multimedia system and addresses how to extract the head feature from an object area. It is observed in images taken from real-time records like a video, there is always a variance in human behavior, such as the position, size, etc. of the person being tracked or recorded. This study discusses how to extract and track multiple objects based on context as opposed to a single object. Via cascade extraction, the proposed system allows tracking of more than one human at a time. For this process, an extraction method based on internal and external contexts, which defines features to distinguish a human, is proposed. The proposed method defines shapes of shoulder and head area to recognize the head-shape of a human, and creates an extractor according to its edge information and geometrical shapes context. In this paper, humans in images are extracted and recognized using contexts and profiles. The proposed method is compared with a single face detector system and it shows better performance in terms of precision and speed. This trace information can be applied in safety care system. Extractions can be improved by validating the image using a context based detector when there are duplicated images.     

A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning

We propose an approach for neuro-fuzzy system modeling. A neuro-fuzzy system for a given set of input-output data is obtained in two steps. First, the data set is partitioned automatically into a set of clusters based on input-similarity and output-similarity tests. Membership functions associated with each cluster are defined according to statistical means and variances of the data points included in the cluster. Then, a fuzzy IF-THEN rule is extracted from each cluster to form a fuzzy rule-base. Second, a fuzzy neural network is constructed accordingly and parameters are refined to increase the precision of the fuzzy rule-base. To decrease the size of the search space and to speed up the convergence, we develop a hybrid learning algorithm which combines a recursive singular value decomposition-based least squares estimator and the gradient descent method. The proposed approach has advantages of determining the number of rules automatically and matching membership functions closely with the real distribution of the training data points. Besides, it learns faster, consumes less memory, and produces lower approximation errors than other methods.     

基于级联稀疏表示分类器的人脸识别算法

针对基于稀疏表示的分类器算法复杂度高、识别速度较慢的问题,提出了基于级联稀疏表示分类器的人脸识别算法。该算法采用级联的思想,通过多次重复使用基于稀疏表示的分类器,逐级精确确定待分类样本所在的类,降低了计算复杂度和识别难度,达到了识别率高、鲁棒性强、识别速度快的目标。