基于小波变换和倒谱分析的腭裂高鼻音等级自动识别

为实现对腭裂高鼻音等级的自动识别,通过对语音信号小波处理和特征提取方法的综合研究,提出基于小波分解系数倒谱特征的腭裂高鼻音等级自动识别算法。目前,研究人员对腭裂语音的研究多基于MFCC、Teager能量、香农能量等特征,识别正确率偏低,且计算量过大。文中对4种等级腭裂高鼻音的1789个元音\a\语音数据提取小波分解系数倒谱特征参数,使用KNN分类器对4种不同等级的高鼻音进行自动识别,将识别结果与MFCC、LPCC、基音周期、共振峰和短时能量共5种经典声学特征的识别结果作比较,同时使用SVM分类器对不同等级的腭裂高鼻音进行自动识别,并与KNN分类器进行对比。实验结果表明,基于小波分解系数倒谱特征的识别结果优于经典声学特征,且KNN分类器的识别结果优于SVM分类器。小波分解系数倒谱特征在KNN中的识别率最高达到91.67%,在SVM中达到87.60%,经典声学特征在KNN分类器中的识别率为21.69%~84.54%,在SVM中的识别率为30.61%~78.24%。     

适应智能质量控制的多分类支持向量机

分析了现有控制图识别器在实际应用中存在的缺陷，并提出了一种基于支持向量机(SVM)的新方法．为了克服HAH多分类SVM(HAH SVM)的缺陷，提高识别速度和准确率，设计了一种有针对性的SVM多分类器进行模式识别．仿真实验结果表明，该方法相对现有的BP和HAH SVM方法能得到更高的识别率和识别速度，适合于工序的实时在线控制．     

一种基于动态贝叶斯网络的人体动作识别方法

针对人体动作识别过程中存在的效率及准确率问题,提出了一种基于混合贝叶斯网络模型的人体动作识别方法。通过Kinect采集人体动作RGB-D信息,采用OpenNi提取关节点信息并计算躯干角度,使用后验概率动态调整SVM分类器和朴素贝叶斯分类器权重,能够识别多种不同动作,使两个分类器互为补充,增加识别率。最后通过与单分类器的对比试验,验证了该算法具有更高的效率和识别率。     

基于支持向量机的白细胞自动识别

考虑到支持向量机(SVM)在训练样本有限的情况下处理高维数据上的优势,鉴于白细胞多光谱图像数据维数高的特点,为提高白细胞识别的速度和精度,采用支持向量机对白细胞的多值分类问题进行了研究,设计并实现了核函数为二值径向函数(RBF)的分类器,实验结果表明,该分类器有效地解决了白细胞的识别速度和精度问题,识别率达到了89.02%.     

基于D—S证据理论的表情识别技术

在情感计算理论基础上,提出了基于D-S理论的信息融合的表情识别技术,设计并实现了系统IFFER.在表情识别模块中的分类器训练采用JAFFE表情库.识别中首先利用色度匹配及亮度匹配将人脸图像进行眼部及嘴部的分割,再分别用训练好的眼部SVM分类器及嘴部SVM分类器进行识别,将识别后的结果利用D-S证据理论进行融合.实验结果表明,对分割后的两部分图像进行识别,无论从训练上还是识别上,数据的维数都大大减少,提高了效率.在识别率上,融合后的结果相对于融合前的有显著的提高.     

基于集成学习支持向量机的步态识别

提出一种基于Bagging算法和SVM的步态识别方法。首先应用背景差分法分割出运动人体轮廓,然后将人体分为多个可变区域,并通过计算获取特征向量。采用SVM分类器进行分类识别,为了提高SVM的识别率,采用Bagging算法对分类结果进行分类集成,实验结果表明,该算法取得了很好地识别性能。     

基于随机森林的直升机飞行状态识别方法

针对直升机飞行状态识别训练样本数据少而导致识别率不高的问题,提出一种基于随机森林的直升机飞行状态识别方法。首先利用去野点、限幅、平滑处理对飞行数据进行预处理,并根据特征参数将飞行状态分为8个小类;然后利用随机森林识别率较高的特点,对每一小类进行随机森林分类器设计;最后利用训练样本训练每个随机森林分类器,并将训练好的随机森林分类器识别直升机全起落飞行状态。以某型直升机实飞数据作为实验数据,将该方法与RBF神经网络法和SVM法进行对比实验,结果表明在小样本情况下该方法识别率有明显提高,识别速度也有所提高,可为直升机寿命预测提供依据。     

基于SVM的手写体数字快速识别方法研究

手写体数字识别是图像处理与模式识别中具有较高实用价值的研究热点之一。在保证较高识别精度的前提下,为提高手写体数字的识别速度,提出了一种基于SVM的快速手写体数字识别方法。该方法通过各类别在特征空间中的可分性强度确定SVM最优核参数,快速训练出SVM分类器对手写体数字进行分类识别。由于可分性强度的计算是一个简单的迭代过程,所需时间远小于传统参数优化方法中训练相应SVM分类器所需时间,故参数确定时间被大大缩减,训练速度得到相应提高,从而加快了手写体数字的识别过程,同时保证了较好的分类准确率。通过对MNIST手写体数字库的实验验证,结果表明该算法是可行有效的。     

基于CBP-TOP特征的人脸表情识别*

针对人脸表情时空域特征信息的有效提取,本文提出了一种CBP-TOP（Centralized Binary Patterns From Three Orthogonal Panels）特征和SVM分类器相结合的人脸表情识别新方法。该方法首先将原始图像序列进行图像预处理,包括人脸检测、图像截取和图像尺度归一化,然后用CBP-TOP算子对图像序列进行分块提取特征,最后采用SVM分类器进行表情识别。实验结果表明,该方法能更有效提取图像序列的运动特征和动态纹理信息,提高了表情识别的准确率。和VLBP特征相比, CBP-TOP特征在表情识别中具有更高的识别率和更快的识别速度。     

基于SVM的脱机手写汉字识别

为了对手写体汉字进行快速准确的识别,本文应用SVM和三种特征识别汉字.经过实验,它的处理速度和识别率都令人满意,如果组合成多分类器可以得到更高的识别率.     

基于增量学习支持向量机的音频例子识别与检索

音频例子识别与检索的主要任务是构造一个良好的分类学习机，而在构造过程中，从含有冗余样本的训练库中选择最佳训练例子、节省学习机的训练时间是构造分类机面临的一个挑战，尤其是对含有大样本训练库音频例子的识别．由于支持向量是支持向量机中的关键例子，提出了增量学习支持向量机训练算法．在这个算法中，训练样本被分成训练子库按批次进行训练，每次训练中，只保留支持向量，去除非支持向量．与普通和减量支持向量机对比的实验表明，算法在显著减少训练时间前提下，取得了良好的识别检索正确率．     

分片支撑矢量机

文中借鉴了分段线性识别的基本思想,提出了分片支撑矢量机模型.该模型首先将特征空间剖分成若干子空间,在每个子空间中基于支撑矢量机构造一个最优分类面,然后,将各个分类面链接起来构成一个分片最优分类面以逼近理论上的最优分类超曲面.同时,文中还从理论上分析探讨了其推广能力的界,为分片支撑矢量机模型提供了坚实的基础.最后,经典双螺旋线数据实验结果表明,相对于传统支撑矢量机,分片支撑矢量机的计算速度、分类能力以及推广能力均有了明显提高.     

一种基于近似支撑矢量机(PSVM)的交通目标分类方法

本文介绍了支撑向量机的特点,给出了实际应用中传统支撑矢量机存在的问题。为了克服支撑矢量机算法的不足,引入了一种近似支撑矢量机（PSVM）算法,并将此算法用于交通目标的分类识别。实验结果表明此算法比BP神经网络法准确率高,比传统的SVM法的效率高。     

基于FLD特征提取的SVM人脸表情识别方法

摘 要 本文通Fisher’s Linear Discriminant(FLD)提取静态人脸表情特征,采用“一对一”支持向量机分类器进行了多种表情识别。在JAFFE人脸表情库上分别进行了测试人参与训练和不参与训练两种方案仿真实验,并与最近邻分类器进行比较,支持向量机都取得了更好的识别结果,说明了支持向量机分类器应用于表情识别是可行的     

Multicategory Proximal Support Vector Machine Classifiers

Given a dataset, each element of which labeled by one of k labels, we construct by a very fast algorithm, a k-category proximal support vector machine (PSVM) classifier. Proximal support vector machines and related approaches (Fung & Mangasarian, 2001; Suykens & Vandewalle, 1999) can be interpreted as ridge regression applied to classification problems (Evgeniou, Pontil, & Poggio, 2000). Extensive computational results have shown the effectiveness of PSVM for two-class classification problems where the separating plane is constructed in time that can be as little as two orders of magnitude shorter than that of conventional support vector machines. When PSVM is applied to problems with more than two classes, the well known one-from-the-rest approach is a natural choice in order to take advantage of its fast performance. However, there is a drawback associated with this one-from-the-rest approach. The resulting two-class problems are often very unbalanced, leading in some cases to poor performance. We propose balancing the k classes and a novel Newton refinement modification to PSVM in order to deal with this problem. Computational results indicate that these two modifications preserve the speed of PSVM while often leading to significant test set improvement over a plain PSVM one-from-the-rest application. The modified approach is considerably faster than other one-from-the-rest methods that use conventional SVM formulations, while still giving comparable test set correctness.Editor Shai Ben-David     

A hybrid discriminative/generative approach to protein fold recognition

There are two standard approaches to the classification task: generative, which use training data to estimate a probability model for each class, and discriminative, which try to construct flexible decision boundaries between the classes. An ideal classifier should combine these two approaches. In this paper a classifier combining the well-known support vector machine (SVM) classifier with regularized discriminant analysis (RDA) classifier is presented. The hybrid classifier is used for protein structure prediction which is one of the most important goals pursued by bioinformatics. The obtained results are promising, the hybrid classifier achieves better result than the SVM or RDA classifiers alone. The proposed method achieves higher recognition ratio than other methods described in the literature.     

A fast classification strategy for SVM on the large-scale high-dimensional datasets

The challenges of the classification for the large-scale and high-dimensional datasets are: (1) It requires huge computational burden in the training phase and in the classification phase; (2) it needs large storage requirement to save many training data; and (3) it is difficult to determine decision rules in the high-dimensional data. Nonlinear support vector machine (SVM) is a popular classifier, and it performs well on a high-dimensional dataset. However, it easily leads overfitting problem especially when the data are not evenly distributed. Recently, profile support vector machine (PSVM) is proposed to solve this problem. Because local learning is superior to global learning, multiple linear SVM models are trained to get similar performance to a nonlinear SVM model. However, it is inefficient in the training phase. In this paper, we proposed a fast classification strategy for PSVM to speed up the training time and the classification time. We first choose border samples near the decision boundary from training samples. Then, the reduced training samples are clustered to several local subsets through MagKmeans algorithm. In the paper, we proposed a fast search method to find the optimal solution for MagKmeans algorithm. Each cluster is used to learn multiple linear SVM models. Both artificial datasets and real datasets are used to evaluate the performance of the proposed method. In the experimental result, the proposed method prevents overfitting and underfitting problems. Moreover, the proposed strategy is effective and efficient.     

模糊临近支持向量机

临近支持向量机(ProximalSupportVectorMachine)是近年提出的一种新的支持向量机。尽管在处理模式分类问题中速度远远超过传统支持向量机,临近支持向量机理论没有考虑不同输入样本点可能会对最优分类超平面的形成产生不同影响。文章给不同的训练样本赋予不同的模糊隶属度,以此来改进临近支持向量机。实验证明这种改进后的模糊临近支持向量机拥有一些临近支持向量机所没有的性能。     

PSVM: a preference-enhanced SVM model using preference data for classification

Classification is an essential task in data mining, machine learning and pattern recognition areas. Conventional classification models focus on distinctive samples from different categories. There are fine-grained differences between data instances within a particular category. These differences form the preference information that is essential for human learning, and, in our view, could also be helpful for classification models. In this paper, we propose a preference-enhanced support vector machine (PSVM), that incorporates preference-pair data as a specific type of supplementary information into SVM. Additionally, we propose a two-layer heuristic sampling method to obtain effective preference-pairs, and an extended sequential minimal optimization (SMO) algorithm to fit PSVM. To evaluate our model, we use the task of knowledge base acceleration-cumulative citation recommendation (KBA-CCR) on the TREC-KBA-2012 dataset and seven other datasets from UCI, StatLib and mldata.org. The experimental results show that our proposed PSVM exhibits high performance with official evaluation metrics.     

增量密度加权近似支持向量机

近似支持向量机((PSVM)是一个正则化最小二乘问题,有解析解,但是它失去了支持向量机(SVM)的稀疏 性,使得所有的训练样例都成为支持向量。为了有效地控制近似支持向量机的稀疏性,提出了增量密度加权近似支持 向量机(mWPSVM),它在训练集中选取最基本的支持向量。实验表明,IvWPSVM方法与SVM, PSVM和DWPS- VM方法相比,其精度相似,收敛速度快,可有效地控制近似支持向量机的稀疏性。