基于后验概率的SVM决策树多类分类算法

后验概率支持向量机方法对孤立点和噪声具有鲁棒性,并且可以减少支持向量的数量,从而降低计算复杂度。因此,针对最近提出的快速分类算法c-BTS,引入样本的后验概率,提出了一种基于后验概率的SVM决策树算法P2BTS。实验结果证明,基于后验概率的支持向量机决策树P2BTS比c-BTS的分类精度更高,且所需的二类分类器个数减少,在一定程度上降低了P2BTS分类决策的时间和比较的次数,提高了分类效率。     

后验概率支持向量机在企业信用评级中的应用

在支持向量机(Support Vector Machine)的分类问题中,训练样本的分类信息总是确定的,由此得到的分类指示函数也总是对新样本给出确定的分类信息,但是这种情况对一些不确定性问题并不恰当.利用贝叶斯规则,将样本的后验概率与传统支持向量机结合,得到了基于后验概率的支持向量机.在具体的算法上,引入了一个经验性的方法得到样本的后验概率.以某评级机构提供的企业信用评估数据库为研究对象.     

基于最大熵估计的支持向量机概率建模

提出一种基于最大熵估计的支持向量机概率建模方法．针对传统的支持向量机方法不能提供后验概率的输出问题,从信息熵的角度采用最大熵估计方法,直接对支持向量机输出进行后验概率建模．实验结果表明,与同类算法相比,所提出的基于最大熵估计的概率建模方法具有优良的性能．     

一种多类支持向量机概率建模新方法

在支持向量机多类分类问题输出概率建模中,提出了一种直接求解后验概率的概率建模新方法。在对多个两类支持向量机分类器的输出概率进行组合时,该方法充分考虑了各个两类支持向量机分类器的差异,并以后验概率作为各个两类支持向量机分类器的权系数。仿真图像的实验结果表明,该文提出的直接求解后验概率方法与投票法及Pairwise Coupling方法相比,不仅具有较好的分类性能,而且得到的后验概率具有较好的概率分布形态。     

基于决策树对支持向量机的医学图像分类新方法

针对传统对支持向量机多类分类算法（Multi-TWSVM）中出现的模糊性问题,提出了一种基于遗传算法的决策树对支持向量机（GA-DTTSVM）多类分类算法。GA-DTTSVM用遗传算法对特征数据建立决策树,通过构建决策树可以分离样本的模糊区域,提高模糊区域样本的识别率。在决策树的每个节点上用对支持向量机（TWSVM）训练分类器,最后用训练的分类器进行分类和预测。实验结果表明,与决策树对支持向量机（DTTSVM）多类分类算法以及Multi-TWSVM相比,GA-DTTSVM多类分类算法具有较高的分类精度和较快的训练速度。     

基于CSI改进的WKNN室内定位方法

传统的加权K最近邻算法中以距离作为权值,随着数据维度的增加,计算距离与真实距离的误差越来越大。针对这一问题,提出了一种贝叶斯后验概率的加权K最近邻算法——贝叶斯后验概率(Bayes ian Posterior Probability-Weighted K-Nearest Neighbor,BPP-WKNN)方法。首先用支持向量机算法分类选取测试点的近邻指纹点,其次计算测试点到每个近邻指纹点的贝叶斯后验概率,最后以贝叶斯后验概率的大小作为权值进行BPPWKNN算法定位。实验果表明:与基于曼哈顿距离的加权K最近邻算法和基于欧氏距离的加权K最近邻算法相比,改进后的BPP-WKNN定位算法的定位精确度和稳定性更高;利用支持向量机算法的稀疏性定位完成时间分别缩短了49%与42%。     

后验概率在多分类支持向量机上的应用

支持向量机是基于统计学习理论的一种新的分类规则挖掘方法。在已有多分类支持向量机基础上,首次提出了几何距离多分类支持向量分类器;随后,将二值支持向量机的后验概率输出也推广到多分类问题,避免了使用迭代算法,在快速预测的前提下提高了预测准确率。数值实验的结果表明,这两种方法都具有很好的推广性能,能明显提高分类器对未知样本的分类准确率。     

基于决策支持向量机的中文网页分类器

提出了基于决策支持向量机的中文网页分类算法。把支持向量机方法和二叉决策树的基本思想结合起来构成多类别的分类器，用于中文网页分类，从而减少支持向量机分类器训练样本的数量，提高训练效率。实验表明，该方法训练数据规模大大减少，训练效率较高，同时具有较好的精确率和召回率。     

基于模糊支持向量机的网络入侵检测

针对目前入侵检测算法存在的分类精度或者效率不高的问题,提出了改进的Relief特征提取算法和基于模糊支持向量机的入侵分类算法.通过对NSL_DATA数据集的预处理和主动学习模式,降低了最小二乘支持向量机的二次线性规划问题求解的复杂度.采用决策树的分类模式,提高了数据样本的检测效率和分类精度.     

基于代价敏感支持向量机的推荐系统托攻击检测方法

基于标准支持向量机的托攻击检测方法不能体现由于用户误分代价不同对分类效果带来的影响,提出了一种基于代价敏感支持向量机的托攻击检测新方法,该方法在代价敏感性学习机制下引入支持向量机作为分类工具,对支持向量机输出进行后验概率建模,建立了基于类别隶属度的动态代价函数,更准确地反映不同样本的分类代价,在此基础上设计了代价敏感支持向量机分类器。将该分类器应用在推荐系统托攻击检测中,并与标准的支持向量机方法、代价敏感支持向量机方法进行比较,实验结果表明,本方法可以更精确地控制代价敏感性,进一步提高对攻击用户的检测精度,降低总体的误分类代价。     

Binary tree of SVM: a new fast multiclass training and classification algorithm

We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c-BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity of the original problem. In the training phase, BTS has N-1 binary classifiers in the best situation (N is the number of classes), while it has log/sub 4/3/((N+3)/4) binary tests on average when making a decision. At the same time the upper bound of convergence complexity is determined. The experiments in this paper indicate that maintaining comparable accuracy, BTS is much faster to be trained than other methods. Especially in classification, due to its Log complexity, it is much faster than directed acyclic graph SVM (DAGSVM) and ECOC in problems that have big class number.     

Fast Multiclass SVM Classification Using Decision Tree Based One-Against-All Method

We present an improved version of One-Against-All (OAA) method for multiclass SVM classification based on a decision tree approach. The proposed decision tree based OAA (DT-OAA) is aimed at increasing the classification speed of OAA by using posterior probability estimates of binary SVM outputs. DT-OAA decreases the average number of binary SVM tests required in testing phase to a greater extent when compared to OAA and other multiclass SVM methods. For a balanced multiclass dataset with K classes, under best situation, DT-OAA requires only (K + 1)/2 binary tests on an average as opposed to K binary tests in OAA; however, on imbalanced multiclass datasets we observed DT-OAA to be much faster with proper selection of order in which the binary SVMs are arranged in the decision tree. Computational comparisons on publicly available datasets indicate that the proposed method can achieve almost the same classification accuracy as that of OAA, but is much faster in decision making.     

Automatic speaker age and gender recognition using acoustic and prosodic level information fusion

The paper presents a novel automatic speaker age and gender identification approach which combines seven different methods at both acoustic and prosodic levels to improve the baseline performance. The three baseline subsystems are (1) Gaussian mixture model (GMM) based on mel-frequency cepstral coefficient (MFCC) features, (2) Support vector machine (SVM) based on GMM mean supervectors and (3) SVM based on 450-dimensional utterance level features including acoustic, prosodic and voice quality information. In addition, we propose four subsystems: (1) SVM based on UBM weight posterior probability supervectors using the Bhattacharyya probability product kernel, (2) Sparse representation based on UBM weight posterior probability supervectors, (3) SVM based on GMM maximum likelihood linear regression (MLLR) matrix supervectors and (4) SVM based on the polynomial expansion coefficients of the syllable level prosodic feature contours in voiced speech segments. Contours of pitch, time domain energy, frequency domain harmonic structure energy and formant for each syllable (segmented using energy information in the voiced speech segment) are considered for analysis in subsystem (4). The proposed four subsystems have been demonstrated to be effective and able to achieve competitive results in classifying different age and gender groups. To further improve the overall classification performance, weighted summation based fusion of these seven subsystems at the score level is demonstrated. Experiment results are reported on the development and test set of the 2010 Interspeech Paralinguistic Challenge aGender database. Compared to the SVM baseline system (3), which is the baseline system suggested by the challenge committee, the proposed fusion system achieves 5.6% absolute improvement in unweighted accuracy for the age task and 4.2% for the gender task on the development set. On the final test set, we obtain 3.1% and 3.8% absolute improvement, respectively.     

Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization

Reliability-based design optimization (RBDO) is concerned with designing an engineering system to minimize a cost function subject to the reliability requirement that failure probability should not exceed a threshold. Conventional RBDO methods are less than satisfactory in dealing with discrete design parameters and complex limit state functions (nonlinear and non-differentiable). Methods that are flexible enough to address the concerns above, however, come at a high computational cost. To enhance computational efficiency without sacrificing model flexibility, we propose a new RBDO framework: PS2, which combines Particle Swarm Optimization (PSO), Support Vector Machine (SVM), and Subset Simulation (SS). SS can efficiently estimate small failure probabilities, based on which SVM is adopted to evaluate the reliability of candidate solutions using binary classification. PSO is employed to solve the discrete optimization problem. Primary emphasis is placed upon the cooperation between SVM and PSO. The cooperation is mutually beneficial since the SVM classifier helps PSO evaluate the feasibility of solutions with high efficiency while the optimal solutions obtained by PSO assist in retraining the SVM classifier to attain better accuracy. The PS2 framework is implemented to find the optimal design of a ten-bar truss, whose component sizes are selected from a commercial standard. The reliability constraints are non-differentiable with two failure modes: yield stress and buckling stress. The interactive process between PSO and SVM contributes greatly to the success of the PS2 framework. It is shown that in various trials the PS2 framework consistently outperforms both the double-loop and single-loop approaches in terms of computational efficiency, solution quality, and model flexibility.     

正则化路径上的支持向量机模型组合方法

模型组合是提高支持向量机泛化性的重要方法,但存在计算效率较低的问题。提出一种基于正则化路径上贝叶斯模型平均的支持向量机模型组合方法,在提高支持向量机泛化性的同时,具有较高的计算效率。基于正则化路径算法建立初始模型集,引入对支持向量机的概率解释。模型的先验可看做是一个高斯过程,模型的后验概率通过贝叶斯公式求得,使用贝叶斯模型平均对模型进行组合。在标准数据集上,实验比较了所提出的模型组合方法与交叉验证及广义近似交叉验证（GACV）方法的性能,验证所提出的模型组合方法的有效性。     

语音识别确认中的置信特征和判定算法

提出了一种基于支持向量机的联合多种置信特征进行语音识别确认的判定方法.从待确认语音中提取出分段的后验概率和线性预测编码识别结果置信特征,其中后验概率根据垃圾模型近似计算得到;设计支持向量机分类器联合多种置信特征给出最终确认结果.实验结果表明,所提出的置信特征和支持向量机分类器取得了很好的确认效果.     

基于概率投票策略的多类支持向量机及应用

传统的支持向量机是基于两类问题提出的,如何将其有效地推广至多类分类仍是一个研究的热点问题。在分析比较现有支持向量机多类分类OVO方法存在的问题及缺点的基础上,该文提出一种新的基于概率投票策略的多类分类方法。在该策略中,充分考虑了OVO方法中各个两类支持向量机分类器的差异,并将该差异反映到投票分值上。所提多类支持向量机方法不仅具有较好的分类性能,而且有效解决了传统投票策略中存在的拒分区域问题。将基于概率投票的多分类支持向量机作为关键技术应用于实际齿轮箱故障诊断,并与传统投票策略的结果进行对比,表明所提方法的上述优点。