基于支持向量机的乐音识别

乐音识别的研究早在20世纪60年代就开始了,但是直到最近,识别系统的性能仍不令人满意。本文通过研究乐音的声音和物理特性,提出了利用支持向量机进行乐音识别的新思路,给出了基本的算法框架,并对钢琴的所有单音进行了识别,实验证明该算法提高了识别的快速性和准确性。     

A rough margin based support vector machine

By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers. Similar to the classical SVM, the RMSVM searches for the separating hyper-plane that maximizes the rough margin, defined by the lower and upper margin. In this way, more data points are adaptively considered rather than the few extreme value points used in the classical SVM. In addition, different support vectors may have different effects on the learning of the separating hyper-plane depending on their positions in the rough margin. Points in the lower margin have more effects than those in the boundary of the rough margin. From experimental results on six benchmark datasets, the classification accuracy of this algorithm is improved without additional computational expense compared with the classical ν-SVM.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

基于特征可分性的核函数自适应构造

核函数的选择与构造是支撑向量机研究中的关键问题和难点.该文针对该问题,首先讨论了特征空间的线性可分性,推导了其判别条件.然后,根据特征完全可分条件,基于函数逼近论和核函数的基本性质,提出了自适应多项式核函数和B-样条核函数模型,给出了模型参数的估计算法.实测数据仿真实验结果表明,与经典的核函数相比,该文提出的算法在分类性能上取得了明显改善.     

上下文感知服务器中基于预测的负载均衡算法

在上下文感知服务器中,需要收集大量的上下文信息并对其进行处理。必须通过服务器集群并采用负载均衡技术才能保证服务器的正常运行。该文提出基于预测负载进行自适应负载均衡的算法,采用支持向量机预测系统运行下一个阶段的负载量,结合当前系统各节点处理情况进行动态负载分配过程,在任务执行过程中,不进行任务的迁移以减少迁移所带来的系统消耗。通过比较表明,在负载变化较大的情况下,算法能够有效地提高服务器性能。     

基于概念的形状分类与识别

基于内容的图像检索已经成为图像检索的一个重要研究方向。其中利用形状特征可以实现形状较为明显的各类图像库的检索。该文对图像形状特征的提取和描述方法进行了较深入地研究,选取其中一部分特征值,分别利用BP神经网络和支持向量机进行形状识别,取得令人满意的实验效果。     

支持向量机序贯最小优化算法推导的改进

已有文献中的支持向量机SMO算法推导过程计算复杂,该文给出一个简洁推导。整个推导过程没有复杂的计算,除了误差函数外,不需引入其它中间变量。     

SVM+BiHMM:基于统计方法的元数据抽取混合模型

提出了一种SVM BiHMM的混合元数据自动抽取方法.该方法基于SVM(support vector machine)和二元HMM(bigram HMM(hidden Markov model),简称BiHMM)理论.二元HMM模型BiHMM在保持模型结构不变的前提下,通过区分首发概率和状态内部发射概率,修改了HMM发射概率计算模型.在SVM BiHMM复合模型中,首先根据规则把论文粗分为论文头、正文以及引文部分,然后建立SVM模型把文本块划分为元数据子类,接着采用Sigmoid双弯曲函数把SVM分类结果用于拟合调整BiHMM模型的单词发射概率,最后用复合模型进行元数据抽取.SVM方法有效考虑了块间联系,BiHMM模型充分考虑了单词在状态内部的位置信息,二者的元数据抽取结果得到了很好的互补和修正,实验评测结果表明,SVM BiHMM算法的抽取效果优于其他方法.     

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.     

多特征融合的蛋白质相互作用位点预测

蛋白质相互作用位点预测为蛋白质功能和药物设计的理解提供重要线索。而蛋白质的各种特征为蛋白质相互作用位点预测提供了大量有用信息,特别是进化信息、残基序列邻近和空间邻近性。不同的蛋白质特征对蛋白质间的相互作用的贡献也不一样。通过提取蛋白质序列谱、保守性和残基熵,提出了特征融合技术对蛋白质相互作用位点进行研究,采用SVM构建三种预测器,分别对各种不同的特征加以验证,实验结果表明了基于特征融合方法的有效性和正确性。