Maghrebian dialect recognition based on support vector machines and neural network classifiers

This paper investigates the feed forward back propagation neural network (FFBPNN) and the support vector machine (SVM) for the classification of two Maghrebian dialects: Tunisian and Moroccan. The dialect used by the Moroccan speakers is called “La Darijja” and that of Tunisians is called “Darija”. An Automatic Speech Recognition System is implemented in order to identify ten Arabic digits (from zero to nine). The implementation of our present system consists of two phases: The features extraction using a variety of popular hybrid techniques and the classification phase using separately the FFBPNN and the SVM. The experimental results showed that the recognition rates with both approaches have reached 98.3 % with FFBPNN and 97.5 % with SVM.     

Intelligent breast cancer recognition using particle swarm optimization and support vector machines

Breast cancer is the most common cancer among women, except for skin cancer, but early detection of breast cancer improves the chances of survivability. Data mining is widely used for this purpose. As technology develops, large number of breast tumour features are being collected. Using all these features for cancer recognition is expensive and time-consuming. Feature extraction is necessary for increasing the classification accuracy. The goal of this work is to recognise breast cancer using extracted features. To reach this goal, a combination of clustering and classification is used. Particle swarm optimization is used to recognise tumour patterns. The membership degree of each tumour to the patterns is calculated and considered as a new feature. Support vector machine is then employed to classify tumours. Finally this method is analysed in terms of its accuracy, specificity, sensitivity and CPU time consuming using Wisconsin Diagnostic Breast Cancer data set.     

Human motion recognition using support vector machines

We propose a motion recognition strategy that represents each videoclip by a set of filtered images, each of which corresponds to a frame. Using a filtered-image classifier based on support vector machines, we classify a videoclip by applying majority voting over the predicted labels of its filtered images and, for online classification, we identify the most likely type of action at any moment by applying majority voting over the predicted labels of the filtered images within a sliding window. We also define a classification confidence and the associated threshold in both cases, which enable us to identify the existence of an unknown type of motion and, together with the proposed recognition strategy, make it possible to build a real-time motion recognition system that cannot only make classifications in real-time, but also learn new types of motions and recognize them in the future. The proposed strategy is demonstrated on real datasets.     

基于支持向量机的人脸识别系统的研究

首先利用PCA进行人脸图像特征提取,然后将此特征数据作为分类器的输入数据。采用的分类器是利用所谓“相似性”方法构造的多个二类SVM分类器,为了提高识别正确率,在多个SVM的输出之后又增加了一级神经网络训练器。以ORL人脸库做的实验中得到了很好的识别效果。     

支持向量机在字符识别中的应用研究

本文应用SVM对字符图像识别进行实验研究,并在此基础上,研究了SVM对含有高斯噪声的字符图像的识别问题。研究结果表明,SVM能够在有限样本的情况下,获得较高的识别率,是目前小样本学习的最佳解决方案。     

Complex-valued support vector classifiers

In this paper, we present a family of complex-valued support vector classifiers (CSVCs) based on the definition of the complex sign inspired by the modulation in digital communications and the complex-valued kernel functions. We also propose a theorem to construct the complex-valued Mercer kernels and the corresponding kernel function groups. CSVC algorithms include binary (2-state) CSVC (BCSVC), quadrature (4-state) CSVC (QCSVC) and some multi-state CSVCs. In this paper, we focus on QCSVC. For a quadrature complex-valued classification problem, QCSVC is identical to the 4-quadrature amplitude modulation demodulation methods in digital communications. Finally, the simulated experiments confirm the validity and the efficiency of CSVCs.     

Empirical analysis of support vector machine ensemble classifiers

Ensemble classification – combining the results of a set of base learners – has received much attention in the machine learning community and has demonstrated promising capabilities in improving classification accuracy. Compared with neural network or decision tree ensembles, there is no comprehensive empirical research in support vector machine (SVM) ensembles. To fill this void, this paper analyses and compares SVM ensembles with four different ensemble constructing techniques, namely bagging, AdaBoost, Arc-X4 and a modified AdaBoost. Twenty real-world data sets from the UCI repository are used as benchmarks to evaluate and compare the performance of these SVM ensemble classifiers by their classification accuracy. Different kernel functions and different numbers of base SVM learners are tested in the ensembles. The experimental results show that although SVM ensembles are not always better than a single SVM, the SVM bagged ensemble performs as well or better than other methods with a relatively higher generality, particularly SVMs with a polynomial kernel function. Finally, an industrial case study of gear defect detection is conducted to validate the empirical analysis results.     

基于支持向量机的货币识别应用研究

支持向量机是一种新的基于统计学习理论的机器学习算法,它可以应用于小样本、非线性和高维模式识别。研究了支持向量机的学习算法,依据支持向量机的特点采用了相应的货币特征数据获取及预处理方法,提出采用改进SMO训练算法和DAGSVM多值分类算法构建的支持向量机用于货币识别,从而达到对货币高效、准确识别。实验结果证实了该方案的有效性。     

Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

This study presents a computer-aided diagnosis (CAD) system with textural features for classifying benign and malignant breast tumors on medical ultrasound systems. A series of pathologically proven breast tumors were evaluated using the support vector machine (SVM) in the differential diagnosis of breast tumors. The proposed CAD system utilized facile textural features, i.e., block difference of inverse probabilities, block variation of local correlation coefficients and auto-covariance matrix, to identify breast tumor. An SVM classifier using the textual features classified the tumor as benign or malignant. The proposed system identifies breast tumors with a comparatively high accuracy. This can help inexperienced physicians avoid misdiagnosis. The main advantage of the proposed system is that the training and diagnosis procedure of SVM are faster and more stable than that of multilayer perception neural networks. With the expansion of the database, new cases can easily be gathered and used as references. This study dramatically reduces the training and diagnosis time. The SVM is a reliable choice for the proposed CAD system because it is fast and excellent in ultrasound image classification.     

An improved protein fold recognition with support vector machines

Predicting the three‐dimensional structure (fold) of a protein is a key problem in molecular biology. It is also interesting issue for statistical methods recognition. In this paper a multi‐class support vector machine (SVM) classifier is used on a real world data set. The SVM is a binary classifier, but protein fold recognition is a multi‐class problem. So several new approaches to deal with this issue are presented including a modification of the well‐known one‐versus‐one strategy. However, in this strategy the number of different binary classifiers that must be trained is quickly increasing with the number of classes. The methods proposed in this paper show how this problem can be overcome.     

支持向量机在低信噪比语音识别中的应用

采用改进的MFCC语音特征参数(Mel频率离散小波倒谱系数),使用支持向量机作为分类算法,构建了低信噪比环境下的孤立词非特定人语音识别系统,取得了较高的识别率。将实验结果与基于RBF神经网络的识别结果进行比较,结果表明在低信噪比时,SVM的识别率比使用RBF神经网络有较大提高,具有非常好的鲁棒性。     

使用超椭球参数化坐标的支持向量机

基于n维超椭球面坐标变换公式,构造一类核函数--n维超椭球坐标变换核.由于是同维映射,且增大了类间距离,这类核函数在一定程度上改善了支持向量机的性能.与其他核函数(如高斯核)相比,将所构造的核函数用于支持向量机,仅产生了很少的支持向量,因而大大加快了学习速度,改善了泛化性能.数值实验结果表明了所构造的核函数的有效性和正确性.     

Patent value analysis using support vector machines

Receiving patents or licenses is an inevitable act of research in order to protect new ideas leading innovation. Request for patents has increased exponentially in order to legalize the intellectual property. Measuring economical value of each patent has been widely studied in the literature. Majority of the research in this field is focused on the patent driver prospect handled for the patent offices. There are a variety of criteria affecting decisions on each patent right; and predicting the possibility of grant may help the researchers to take some precautions. Objective of this study is to propose a robust model to determine if the appeal has a chance of approval. A case study is run on the patents that are accepted and rejected in home appliance industry to construct an intelligent classification model. The support vector machine, Back-Propagation Network and Bayes classification methods are compared on the proposed model. The proposed model in this study will help the decision makers to predict whether the patent appeal will be accepted. The study is unique with the approach that helps the candidate patent owners.     

Fuzzy c-means based support vector machines classifier for perfume recognition

Identification of more than three perfumes is very difficult for the human nose. It is also a problem to recognize patterns of perfume odor with an electronic nose that has multiple sensors. For this reason, a new hybrid classifier has been presented to identify type of perfume from a closely similar data set of 20 different odors of perfumes. The structure of this hybrid technique is the combination of unsupervised fuzzy clustering c-mean (FCM) and supervised support vector machine (SVM). On the other hand this proposed soft computing technique was compared with the other well-known learning algorithms. The results show that the proposed hybrid algorithm’s accuracy is 97.5% better than the others.     

Speaker recognition using pyramid match kernel based support vector machines

Gaussian mixture model (GMM) based approaches have been commonly used for speaker recognition tasks. Methods for estimation of parameters of GMMs include the expectation-maximization method which is a non-discriminative learning based method. Discriminative classifier based approaches to speaker recognition include support vector machine (SVM) based classifiers using dynamic kernels such as generalized linear discriminant sequence kernel, probabilistic sequence kernel, GMM supervector kernel, GMM-UBM mean interval kernel (GUMI) and intermediate matching kernel. Recently, the pyramid match kernel (PMK) using grids in the feature space as histogram bins and vocabulary-guided PMK (VGPMK) using clusters in the feature space as histogram bins have been proposed for recognition of objects in an image represented as a set of local feature vectors. In PMK, a set of feature vectors is mapped onto a multi-resolution histogram pyramid. The kernel is computed between a pair of examples by comparing the pyramids using a weighted histogram intersection function at each level of pyramid. We propose to use the PMK-based SVM classifier for speaker identification and verification from the speech signal of an utterance represented as a set of local feature vectors. The main issue in building the PMK-based SVM classifier is construction of a pyramid of histograms. We first propose to form hard clusters, using k-means clustering method, with increasing number of clusters at different levels of pyramid to design the codebook-based PMK (CBPMK). Then we propose the GMM-based PMK (GMMPMK) that uses soft clustering. We compare the performance of the GMM-based approaches, and the PMK and other dynamic kernel SVM-based approaches to speaker identification and verification. The 2002 and 2003 NIST speaker recognition corpora are used in evaluation of different approaches to speaker identification and verification. Results of our studies show that the dynamic kernel SVM-based approaches give a significantly better performance than the state-of-the-art GMM-based approaches. For speaker recognition task, the GMMPMK-based SVM gives a performance that is better than that of SVMs using many other dynamic kernels and comparable to that of SVMs using state-of-the-art dynamic kernel, GUMI kernel. The storage requirements of the GMMPMK-based SVMs are less than that of SVMs using any other dynamic kernel.     

Wavelets-based facial expression recognition using a bank of support vector machines

A human face does not play its role in the identification of an individual but also communicates useful information about a person’s emotional state at a particular time. No wonder automatic face expression recognition has become an area of great interest within the computer science, psychology, medicine, and human–computer interaction research communities. Various feature extraction techniques based on statistical to geometrical data have been used for recognition of expressions from static images as well as real-time videos. In this paper, we present a method for automatic recognition of facial expressions from face images by providing discrete wavelet transform features to a bank of seven parallel support vector machines (SVMs). Each SVM is trained to recognize a particular facial expression, so that it is most sensitive to that expression. Multi-classification is achieved by combining multiple SVMs performing binary classification using one-against-all approach. The outputs of all SVMs are combined using a maximum function. The classification efficiency is tested on static images from the publicly available Japanese Female Facial Expression database. The experiments using the proposed method demonstrate promising results.     

Distributed support vector machines

A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes). In several examples, it has been found that a reasonably small amount of information is interchanged among nodes to obtain an SVM solution, which is better than that obtained when classifiers are trained only with the local data and comparable (although a little bit worse) to that of the centralized approach (obtained when all the training data are available at the same place). We propose and analyze two distributed schemes: a "na/spl inodot//spl uml/ve" distributed chunking approach, where raw data (support vectors) are communicated, and the more elaborated distributed semiparametric SVM, which aims at further reducing the total amount of information passed between nodes while providing a privacy-preserving mechanism for information sharing. We show the feasibility of our proposal by evaluating the performance of the algorithms in benchmarks with both synthetic and real-world datasets.     

核函数支持向量机

概述了基于核函数方法的支持向量机。首先简要叙述支持向量机的基本思想和核特征空间,然后重点介绍核函数支持向量机的前沿理论与领先技术,同时描述了核函数支持向量机在关键领域的应用。     

支持向量机的新发展

Vapnik等学者首先提出了实现统计学习理论中结构风险最小化原则的实用算法一支持向量机，比较成功地解决了模式分类问题，其后，机器学习界兴起了研究统计学习理论和支持向量机的热湖，引人瞩目的研究分支有从最优化技术出发改进或改造支持向量机，依据统计学习理论和支持向量机的优点设计新的非线性机器学习算法等，对此，较为系统地回顾了近lO年来算法研究领域的新发展。