基于混合粒子群算法的运动估计研究

针对块匹配运动估计算法中传统搜索方法的不足,提出了一种新的基于混合粒子群的块匹配运动估计算法。在保留系统随机搜索性能的同时根据运动矢量特性合理地设计初始搜索种群,并通过混沌差分进化搜索协同粒子群算法迭代寻优,混沌序列用于优化差分变异算子,以提高算法的精细搜索能力。通过相同点检测技术和恰当的终止计划有效地降低了系统的运算复杂度。经实验测试与验证,该算法在搜索质量和运算复杂度中达到了一种动态平衡的状态,其整体性能高于传统的快速运动估计算法,效果更逼近于穷举搜索法。     

基于支持向量机的多类音频分类

研究一种用支持向量机(SVM)进行多类音频分类的方法,其中引入增广两类分类法(AB法)设计多类分类器。该算法把音频分为四类:音乐、纯语音、带背景音的语音和典型的环境音,并分析了这几类音频的八个区别性特征,包括修正低能量成分比率(MLER)和修正基频(MPF)两个新特征以及频域总能量、子带能量、频率中心等其它六个基本特征,综合考察了不同特征集在基于SVM分类器中的分类精度。实验结果表明,提取的音频特征有效,基于SVM的多类音频分类效果良好。     

ComR: a combined OWL reasoner for ontology classification

Ontology classification, the problem of computing the subsumption hierarchies for classes (atomic concepts), is a core reasoning service provided by Web Ontology Language (OWL) reasoners. Although general-purpose OWL 2 reasoners employ sophisticated optimizations for classification, they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient; however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL fragment. In this paper, we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive SROIQ. To optimize the workload, we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure completeness. During the ontology classification, the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non- EL subontology is handled by a less efficient OWL 2 reasoner. The proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology classification. For the wellknown ontology NCI, the classification time is reduced by 96.9% (resp. 83.7%) compared against the standard reasoner Pellet (resp. the modular reasoner MORe).     

基于数据挖掘的本体构建与重构技术研究

本体理论在知识工程领域得到广泛关注和普遍认可,构建完备且准确的领域本体已经越来越重要,同时,企业知识资源的更新与集成要求本体的不断进化与融合;针对目前本体构建与重构过程中数据处理效率低的问题,运用支持向量机分类及K-均值聚类的方法对本体构建数据进行处理,从文本数据中抽取关注的特定的信息,运用基于二叉树的多分类支持向量机以及支持向量机与K-均值融合的多样本聚类,总结基于分类与聚类的本体构建过程,并以离散型和连续型两种数据样本验证了方法的可行性;最后,在上述框架与理论研究的基础上,设计并开发了面向知识管理的本体工具平台,简单介绍系统的模块功能;实验结果表明,基于数据挖掘的本体构建与重构技术具有良好的应用效果。     

Content-based audio classification and segmentation by using support vector machines

Content-based audio classification and segmentation is a basis for further audio/video analysis. In this paper, we present our work on audio segmentation and classification which employs support vector machines (SVMs). Five audio classes are considered in this paper: silence, music, background sound, pure speech, and non- pure speech which includes speech over music and speech over noise. A sound stream is segmented by classifying each sub-segment into one of these five classes. We have evaluated the performance of SVM on different audio type-pairs classification with testing unit of different- length and compared the performance of SVM, K-Nearest Neighbor (KNN), and Gaussian Mixture Model (GMM). We also evaluated the effectiveness of some new proposed features. Experiments on a database composed of about 4- hour audio data show that the proposed classifier is very efficient on audio classification and segmentation. It also shows the accuracy of the SVM-based method is much better than the method based on KNN and GMM.     

基于支持向量机的音频分类与分割

音频分类与分割是提取音频结构和内容语义的重要手段,是基于内容的音频、视频检索和分析的基础。支持向量机(SVM)是一种有效的统计学习方法。本文提出了一种基于SVM的音频分类算法。将音频分为5类：静音、噪音、音乐、纯语音和带背景音的语音。在分类的基础上,采用3个平滑规则对分类结果进行平滑。分析了SVM分类嚣的分类性能,同时也评估了本文提出的新的音频特征在SVM分类嚣上的分类效果。实验结果显示,基于SVM的音频分类算法分类效果良好,平滑处理后的音频分割结果比较准确。     

A SVM based classification method for homogeneous data

A novel classification method based on SVM is proposed for binary classification tasks of homogeneous data in this paper. The proposed method can effectively predict the binary labeling of the sequence of observation samples in the test set by using the following procedure: we first make different assumptions about the class labeling of this sequence, then we utilize SVM to obtain two classification errors respectively for each assumption, and finally the binary labeling is determined by comparing the obtained two classification errors. The proposed method leverages the homogeneity within the same classes and exploits the difference between different classes, and hence can achieve the effective classification for homogeneous data. Experimental results indicate the power of the proposed method.     

TCBR-HMM: An HMM-based text classifier with a CBR system

This paper presents an innovative solution to model distributed adaptive systems in biomedical environments. We present an original TCBR-HMM (Text Case Based Reasoning-Hidden Markov Model) for biomedical text classification based on document content. The main goal is to propose a more effective classifier than current methods in this environment where the model needs to be adapted to new documents in an iterative learning frame. To demonstrate its achievement, we include a set of experiments, which have been performed on OSHUMED corpus. Our classifier is compared with Naive Bayes and SVM techniques, commonly used in text classification tasks. The results suggest that the TCBR-HMM Model is indeed more suitable for document classification. The model is empirically and statistically comparable to the SVM classifier and outperforms it in terms of time efficiency.     

基于SVM的眼动轨迹解读思维状态的研究

从人们的眼动轨迹来解读人的思维状态已成为应用心理学的研究热点。以四方趣题为研究材料,通过Tobii眼动仪记录被试解题时的眼动轨迹,以眼动轨迹数据中各个AOI的注视持续时间和回视等综合指标的加权值作为特征值训练SVM分类器。经过三种不同的分类任务的实验验证,SVM分类的准确率很高,泛化能力很强,可以作为眼动轨迹分析的分类方法,从而可根据眼动轨迹解读被试解题时的不同策略。     

制造业设计文档的模糊分类

提出一种面向制造业设计文档的模糊分类方法．利用领域本体的层次结构和概念间的语义关系，对设计文档进行结构划分与标注，通过特征词与概念之间的距离和位置重要性计算权重，提高了设计文档分类的准确性．     

基于系统中心本体的分层抽象模型

本体类分层关系的确定,使得能够利用不同抽象度本体类之间的抽象映射自动构建物理世界的分层模型。根据本体类型的不同将已有的本体类定义为对象本体类(Object based Ontology Class),并对流片段(Flow Fragment)的概念加以扩展,提出以系统为中心的流本体类(Flow-based Ontology Class)的概念。定义了流片段的行为不可区分性,从两个不同的方向讨论了流本体类的分层表示:一是对构成流本体类的流片段的不同抽象度分层扩展;二是对构成流本体类的不可区分的流片段的合并分层扩展。给出了流本体类的分层过程,定义了系统级的映射关系,即流片段之间的抽象映射,指出该映射为基于系统中心本体的模型抽象分层过程提供了直接的转换运算。并且,流本体类的分层关系的确定也为基于系统中心本体的模型设计任务提供了可共享和重用的机制。     

Knowledge based Least Squares Twin support vector machines

We propose knowledge based versions of a relatively new family of SVM algorithms based on two non-parallel hyperplanes. Specifically, we consider prior knowledge in the form of multiple polyhedral sets and incorporate the same into the formulation of linear Twin SVM (TWSVM)/Least Squares Twin SVM (LSTWSVM) and term them as knowledge based TWSVM (KBTWSVM)/knowledge based LSTWSVM (KBLSTWSVM). Both of these formulations are capable of generating non-parallel hyperplanes based on real-world data and prior knowledge. We derive the solution of KBLSTWSVM and use it in our computational experiments for comparison against other linear knowledge based SVM formulations. Our experiments show that KBLSTWSVM is a versatile classifier whose solution is extremely simple when compared with other linear knowledge based SVM algorithms.     

基于SVM的大样本数据回归预测改进算法

针对支持向量机回归预测精度与训练样本尺寸不成正比的问题,结合支持向量机分类与回归算法,提出一种大样本数据分类回归预测改进算法。设计训练样本尺寸寻优算法,根据先验知识对样本数据进行人为分类,训练分类模型,基于支持向量机得到各类别样本的回归预测模型,并对数据进行预测。使用上证指数的数据进行实验,结果表明,支持向量机先分类再回归算法预测得到的均方误差达到12.4,低于人工神经网络预测得到的47.8,更远低于支持向量机直接回归预测得到的436.9,验证了该方法的有效性和可行性。     

Bug Prioritization to Facilitate Bug Report Triage

The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolution of important bugs.To address this issue,a recommender may be developed which automatically prioritizes the new bug reports.In this paper,we propose and evaluate a classification based approach to build such a recommender.We use the Na¨ ve Bayes and Support Vector Machine (SVM) classifiers,and present a comparison to evaluate which classifier performs better in terms of accuracy.Since a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a bug.To evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives (NFN) and Nearest False Positives (NFP),which provide insight into the results produced by precision and recall.Our findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than SVM.The highest accuracy is achieved with SVM when categorical and text features are combined for training.     

Large margin mixture of AR models for time series classification

In this paper, we propose the large margin autoregressive (LMAR) model for classification of time series patterns. The parameters of the generative AR models for different classes are estimated using the margin of the boundaries of AR models as the optimization criterion. Models that use a mixture of AR (MAR) models are considered for representing the data that cannot be adequately represented using a single AR model for a class. Based on a mixture model representing each class, we propose the large margin mixture of AR (LMMAR) models. The proposed methods are applied on the simulated time series data, electrocardiogram data, speech data for E-set in English alphabet and electroencephalogram time series data. Performance of the proposed methods is compared with that of support vector machine (SVM) based classifier that uses AR coefficients based features. The proposed methods give a better classification performance compared to the SVM based classifier. Being generative models, the LMAR and LMMAR models provide a generative interpretation that enables utilization of the rejection option in the high risk classification tasks. The proposed methods can also be used for detection of novel time series data.     

一种基于预分类的高效SVM中文网页分类器

中文网页分类技术是数据挖掘研究中的一个热点领域,而支持向量机（SVM）是一种高效的分类识别方法。首先给出了一个基于SVM的中文网页自动分类系统模型,详细介绍了分类过程中涉及的一些关键技术,其中包括网页预处理、特征选择和特征权重计算等。提出了一种利用预置关键词表进行预分类的方法,并详细说明了该方法的原理与实现。实验结果表明,该方法与单独使用SVM分类器相比,不仅大大减少了分类时间,准确率和召回率也明显提高。     

ECG beat classification using particle swarm optimization and support vector machine

In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine (SVM) classifier. The method extracts electrocardiogram’s spectral and three timing interval features. Non-parametric power spectral density (PSD) estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using particle swarm optimization (PSO). These parameters are: Gaussian radial basis function (GRBF) kernel parameter σ and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid particle swarm optimization-support vector machine (SVMPSO) classification method offers significantly improved performance over the SVM which has constant and manually extracted parameter.     

An enhanced swarm intelligence clustering-based RBFNN classifier and its application in deep Web sources classification

The central problem in training a radial basis function neural network (RBFNN) is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose an enhanced swarm intelligence clustering (ESIC) method to select hidden layer neurons, and then, train a cosine RBFNN based on the gradient descent learning process. Also, we apply this new method for classification of deep Web sources. Experimental results show that the average Precision, Recall and F of our ESIC-based RBFNN classifier achieve higher performance than BP, Support Vector Machines (SVM) and OLS RBF for our deep Web sources classification problems.     

基于多角度LBP特征的三维0.3人脸性别分类

人脸性别分类是一个富有挑战的研究方向,目前的研究尚不完善.本文提出一种三维人脸的性别分类方法, 首先对数据集进行局部区域最近邻点迭代算法(Iterative closest point, ICP)匹配,自动实现人脸正向姿态校正;对数据集人脸统一做俯仰角度的旋转, 从不同视角上提取基于深度缩略图的多角度LBP (Local binary patterns)特征;再由支持向量机(Support vector machine, SVM)分类器完成训练分类. 该方法在CASIA数据库上实验,对全库中性表情人脸进行性别分类,可以得到最高98.374%的正确率.     

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic (PV) power is closely related to the weather, many PV enterprises install weather instruments to monitor the working state of the PV power system. With the development of the soft measurement technology, the instrumental method seems obsolete and involves high cost. This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data. By this method, the weather types are deduced by data analysis, instead of weather instrument. A better fault detection is obtained by using the support vector machines (SVM) and comparing the predicted and the actual weather. The model of the weather prediction is established by a direct SVM for training multiclass predictors. Although SVM is suitable for classification, the classified results depend on the type of the kernel, the parameters of the kernel, and the soft margin coefficient, which are difficult to choose. In this paper, these parameters are optimized by particle swarm optimization (PSO) algorithm in anticipation of good prediction results can be achieved. Prediction results show that this method is feasible and effective.