改进证据理论的多生物特征融合方法

多生物特征融合考虑了个体的多种生理或行为特征,因而能显著地改善系统的识别性能,成为生物特征识别技术未来发展趋势之一。利用训练样本的识别率和误识率,提出了基于证据理论的多生物特征融合识别方法;对各识别专家的识别率和误识率进行分析,提出了一种基于累积频率和证据理论（Cumulative Frequency based D-S,CFDS）的多生物特征融合方法;通过几个实验证明了改进的D-S算法的有效性,提高了合成结果的可靠性。     

A simple decomposition algorithm for support vector machines with polynomial-time convergence

Support vector machines (SVMs) are a new and important tool in data classification. Recently much attention has been devoted to large scale data classifications where decomposition methods for SVMs play an important role.So far, several decomposition algorithms for SVMs have been proposed and applied in practice. The algorithms proposed recently and based on rate certifying pair/set provide very attractive features compared with many other decomposition algorithms. They converge not only with finite termination but also in polynomial time. However, it is difficult to reach a good balance between low computational cost and fast convergence.In this paper, we propose a new simple decomposition algorithm based on a new philosophy on working set selection. It has been proven that the working set selected by the new algorithm is a rate certifying set. Further, compared with the existing algorithms based on rate certifying pair/set, our algorithm provides a very good feature in combination of lower computational complexity and faster convergence.     

一种改进的DAGSVM 手势识别方法及其应用

在支持向量机多分类方法基础上,提出了一种改进的有向无环图支持向量机( Directed Acyclic Graph Support Vector Machine,DAGSVM) 手势识别方法。首先根据Kinect 采集到 的场景深度信息将前景和背景分开,分割得到手,然后提取其特征向量,利用特征向量训练多 个SVM 两分类器,采用DAG 拓扑结构构成DAGSVM 多分类器,并对其结构排序进行改进。 实验证明,与其他支持向量机多分类方法相比,改进后的DAGSVM 分类器能够达到更高的识 别率,并将这个手势识别方法用于智能轮椅的控制上,收到了良好的效果。     

HMM/SVM segmentation and labelling of Arabic speech for speech recognition applications

Building a large vocabulary continuous speech recognition (LVCSR) system requires a lot of hours of segmented and labelled speech data. Arabic language, as many other low-resourced languages, lacks such data, but the use of automatic segmentation proved to be a good alternative to make these resources available. In this paper, we suggest the combination of hidden Markov models (HMMs) and support vector machines (SVMs) to segment and to label the speech waveform into phoneme units. HMMs generate the sequence of phonemes and their frontiers; the SVM refines the frontiers and corrects the labels. The obtained segmented and labelled units may serve as a training set for speech recognition applications. The HMM/SVM segmentation algorithm is assessed using both the hit rate and the word error rate (WER); the resulting scores were compared to those provided by the manual segmentation and to those provided by the well-known embedded learning algorithm. The results show that the speech recognizer built upon the HMM/SVM segmentation outperforms in terms of WER the one built upon the embedded learning segmentation of about 0.05%, even in noisy background.     

基于支撑向量机的空瓶智能检测方法

为实现空瓶的智能检测,对空瓶检测的机器视觉方法进行了深入研究.论述了如何进行处理区域的标定,以及提取空瓶图像特征的方法.在提取了特征之后,提出用遗传支撑向量机算法来进行分类决策,支撑向量机具有较好的推广能力.同时采用遗传算法来优化选择支撑向量机的参数,以保证支撑向量机具有优良的分类性能.实验表明,采用这种方法检测空瓶,其检测准确率可达95%以上.     

一种用于文本分类的语义SVM及其在线学习算法

该文利用SVM在小训练样本集条件下仍有高泛化能力的特性,结合文本分类问题中同类别文本的特征在特征空间中具有聚类性分布的特点,提出一种使用语义中心集代替原训练样本集作为训练样本和支持向量的SVM:语义SVM。文中给出语义中心集的生成步骤,进而给出语义SVM的在线学习(在线分类知识积累)算法框架,以及基于SMO算法的在线学习算法的实现。实验结果说明语义SVM及其在线学习算法具有巨大的应用潜力:不仅在线学习速度和分类速度相对于标准SVM及其简单增量算法有数量级提高,而且分类准确率方面具有一定优势。     

Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification

Support vector machines (SVMs) are essentially binary classifiers. To improve their applicability, several methods have been suggested for extending SVMs for multi-classification, including one-versus-one (1-v-1), one-versus-rest (1-v-r) and DAGSVM. In this paper, we first describe how binary classification with SVMs can be interpreted using rough sets. A rough set approach to SVM classification removes the necessity of exact classification and is especially useful when dealing with noisy data. Next, by utilizing the boundary region in rough sets, we suggest two new approaches, extensions of 1-v-r and 1-v-1, to SVM multi-classification that allow for an error rate. We explicitly demonstrate how our extended 1-v-r may shorten the training time of the conventional 1-v-r approach. In addition, we show that our 1-v-1 approach may have reduced storage requirements compared to the conventional 1-v-1 and DAGSVM techniques. Our techniques also provide better semantic interpretations of the classification process. The theoretical conclusions are supported by experimental findings involving a synthetic dataset.     

Choosing Multiple Parameters for Support Vector Machines

The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.     

快速的支持向量机多类分类研究

研究了支持向量机多类算法DAGSVM（Direct Acyclic Graph SVM）的速度优势,提出了结合DAGSVM和简化支持向量技术的一种快速支持向量机多类分类方法。该方法一方面减少了一次分类所需的两类支持向量机的数量,另一方面减少了支持向量的数量。实验采用UCI和Statlog数据库的多类数据,并和四种多类方法进行比较,结果表明该方法能有效地加快分类速度。     

An MLP-SVM combination architecture for offline handwritten digit recognition

This paper presents an original hybrid MLP-SVM method for unconstrained handwritten digits recognition. Specialized Support Vector Machines (SVMs) are introduced to improve significantly the multilayer perceptron (MLP) performance in local areas around the separating surfaces between each pair of digit classes, in the input pattern space. This hybrid architecture is based on the idea that the correct digit class almost systematically belongs to the two maximum MLP outputs and that some pairs of digit classes constitute the majority of MLP substitutions (errors). Specialized local SVMs are introduced to detect the correct class among these two classification hypotheses. The hybrid MLP-SVM recognizer achieves a recognition rate of 98.01%98.01\% , for real mail zipcode digits recognition task. By introducing a rejection mechanism based on the distances provided by the local SVMs, the error/reject trade-off performance of our recognition system is better than several classifiers reported in recent research.     

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.     

Ginisupport vector machines for segmental minimum Bayes risk decoding of continuous speech

We describe the use of support vector machines (SVMs) for continuous speech recognition by incorporating them in segmental minimum Bayes risk decoding. Lattice cutting is used to convert the Automatic Speech Recognition search space into sequences of smaller recognition problems. SVMs are then trained as discriminative models over each of these problems and used in a rescoring framework. We pose the estimation of a posterior distribution over hypotheses in these regions of acoustic confusion as a logistic regression problem. We also show that GiniSVMs can be used as an approximation technique to estimate the parameters of the logistic regression problem. On a small vocabulary recognition task we show that the use of GiniSVMs can improve the performance of a well trained hidden Markov model system trained under the Maximum Mutual Information criterion. We also find that it is possible to derive reliable confidence scores over the GiniSVM hypotheses and that these can be used to good effect in hypothesis combination. We discuss the problems that we expect to encounter in extending this approach to large vocabulary continuous speech recognition and describe initial investigation of constrained estimation techniques to derive feature spaces for SVMs.     

Texture classification using the support vector machines

In recent years, support vector machines (SVMs) have demonstrated excellent performance in a variety of pattern recognition problems. In this paper, we apply SVMs for texture classification, using translation-invariant features generated from the discrete wavelet frame transform. To alleviate the problem of selecting the right kernel parameter in the SVM, we use a fusion scheme based on multiple SVMs, each with a different setting of the kernel parameter. Compared to the traditional Bayes classifier and the learning vector quantization algorithm, SVMs, and, in particular, the fused output from multiple SVMs, produce more accurate classification results on the Brodatz texture album.     

Support vector machines for face recognition

Support vector machines (SVMs) have been recently proposed as a new learning network for bipartite pattern recognition. In this paper, SVMs incorporated with a binary tree recognition strategy are proposed to tackle the multi-class face recognition problem. The binary tree extends naturally, the pairwise discrimination capability of the SVMs to the multi-class scenario. Two face databases are used to evaluate the proposed method. The performance of the SVMs based face recognition is compared with the standard eigenface approach, and also the more recently proposed algorithm called the nearest feature line (NFL).     

基于支持向量机的仓储物害虫分类识别研究

论文提出了基于支持向量机的仓储物害虫的分类识别问题。首先利用计算机数字图像处理技术,综合提取静态仓储物害虫图像的数理统计特征、纹理特征和几何形状特征,并采用支持向量机进行分类识别。实验表明该方法对复杂模式的识别能力较强,达到了较高的识别率,这也为仓储物害虫的快速鉴定和分类研究开辟了新的途径。     

基于纠错输出编码的人脸表情识别

多分类问题一直是模式识别领域的一个热点,提出了一种基于纠错输出编码和支持向量机的多分类器算法。根据通信编码理论设计纠错输出编码矩阵;按照该编码矩阵设计若干个互不相关的子支持向量机,根据编码原理将它们融合为一个多分类器。为了验证本分类器的有效性,采用Gabor小波提取人脸表情特征,应用二元主成分（2DPCA）分析法对提取的特征进行降维处理,应用该分类器进行了人脸表情的识别。实验结果表明,提出的方法能有效提高人脸表情的识别率,并具有极好的鲁棒性。     

基于支持向量机的文本兼类标注

该文分析了现有多类别支持向量机分类器的特点及DAGSVM的优势,并结合模糊技术改造DAGSVM使之能进行兼类标注的多类别分类。改进后的FDAGSVM采用模糊决策面代替了DAGSVM的分明决策面,使判决过程适应兼类标注的要求,克服了传统的多类别分类支持向量机必然将样本分入某一类别的不足。基准数据的兼类标注多类别分类试验表明,FDAGSVM在文本的兼类标注分类中表现出较好的性能。     

Sparse kernel SVMs via cutting-plane training

We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. This results in two benefits. First, the added flexibility makes it possible to find sparser solutions of good quality, substantially speeding-up prediction. Second, the improved sparsity can also make training of Kernel SVMs more efficient, especially for high-dimensional and sparse data (e.g. text classification). This has the potential to make training of Kernel SVMs tractable for large training sets, where conventional methods scale quadratically due to the linear growth of the number of SVs. In addition to a theoretical analysis of the algorithm, we also present an empirical evaluation.     

基于无监督聚类的约简支撑向量机

为解决标准支撑向量机算法所面临的巨大的计算量问题,Lee和Mangasarian提出了约简支撑向量机算法;但他们选取的“支撑向量”是从训练样本里面任意选的,其分类结果受随机性影响比较大。该文利用简单的无监督聚类算法,在样本空间中选取了一些具有较强代表性的样本作为“支撑向量”,再运用约简支撑向量机算法,有效地减少了运算量。实验验证文中方法可以用较少的“支撑向量”来得到较高的识别率,同时运行时间也大大缩短。     

基于F-SVMs的多模型建模方法

针对全局模型难以精确描述复杂工业过程的问题,提出一种基于模糊支持向量机(F-SVMs)的多模型(F-SVMs MM)建模方法。用模糊支持向量分类算法(F-SVC)对输入数据进行预处理,得到多模型模糊隶属度;用模糊支持回归算法(F-SVR)建立多模型(MM)估计器。应用该方法对pH中和滴定过程进行建模,仿真结果表明,F-SVMs MM跟踪性能好、泛化能力强,比USOCPN方法和标准支持向量机(SVMs)方法具有更好的性能和推广能力。