关键词： 数据挖掘分类预测判定树归纳线性回归保持法  

摘要： 数据挖掘技术是信息时代的宠儿,而分类和预测是数据分析的两种基本形式,能预测未知数据的趋势。本文主要介绍了何为数据的分类和预测,并且通过判定树归纳细化了数据分类的划分步骤;通过介绍线性回归、多元回归以及非线性回归等预测方法加深了对数据预测的认识;并介绍了分类法准确率评估方法以及分类和预测的异同点。

关键词： 数据挖掘   SVC   SVR   电信故障预测  

摘要： 基于数据挖掘技术,针对电信故障海量数据特点,合理选择属性值和标签值,运用交叉验证、网格划分、遗传算法和粒子群算法进行参数寻优,运用支持向量机SVM理论,建立电信故障分类模型和预测模型。通过仿真分析,并且与电信故障实际数据对比,表明该分类模型和预测模型的精度高,误差小,为今后控制电信故障,改善网络运行质量提供理论依据和数据支持。

关键词： 核函数   核机器学习方法   生物信息学   机器学习   支持向量机  

摘要： 核机器学习方法是一种基于统计学习理论和核函数的机器学习方法。本文阐述了核机器学习方法的基本原理,介绍了目前常用的几种核机器学习方法,给出了构造新的核函数的原则,总结了结构数据核函数的研究现状。在此基础上,以生物信息学为背景,着重指出了当前序列数据的核函数在生物信息学中的应用进展。

关键词： 事件代词指代消歧   机器学习方法   实例生成   特征选择  

摘要： 与实体指代不同,事件指代因为其先行词候选是一个事件,与名词性的指代词具有完全不同的语义分类体系,因此适用于实体指代消歧的大多数特征都不能用于事件指代消歧。该文给出了一个基于机器学习方法的事件代词指代消歧平台,详细介绍了平台的实例生成和特征选择过程,并给出了平台在OntoNotes3.0语料上的事件代词指代消歧的结果,对结果进行了分析。从实验结果可以看到,给出的平台获得了较好的系统性能。

关键词： Empirical Validation   Object Oriented   Receiver Operating Characteristics   Statistical Methods   Machine Learning   Fault Prediction  

摘要： An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods

关键词： End Point   data types   response curves   Machine Learning   Learning methods   Phase diagrams   tautomer   nomenclature   accounting   Modeling  

摘要： This paper is focused on modern approaches to machine learning, most of which are as yet used infrequently or not at all in chemoinformatics. Machine learning methods are characterized in terms of the "modes of statistical inference" and "modeling levels" nomenclature and by considering different facets of the modeling with respect to input/ouput matching, data types, models duality, and models inference. Particular attention is paid to new approaches and concepts that may provide efficient solutions of common problems in chemoinformatics: improvement of predictive performance of structure property (activity) models, generation of structures possessing desirable properties, model applicability domain, modeling of properties with functional endpoints (e.g., phase diagrams and dose response curves), and accounting for multiple molecular species (e.g., conformers or tautomers).

关键词： 高斯朴素贝叶斯   支持向量机   功能磁共振成像   特征选择  

摘要： 特征选择和分类是脑功能磁共振成像(fMRI)数据分析的核心问题。针对fMRI高维数据,特征选择分两步,选取感兴趣脑区,选择最能区分刺激任务体素。该方法简单,稳定,符合人的思维逻辑。分类器选择高斯朴素贝叶斯(GNB)和支持向量机(SVM),评估该特征选择方法。实验结果表明,该方法有效提高了分类速度,分类准确度也得到很大提高。对分类方法进行比较,SVM总体上优于GNB。

关键词： 蛋白质功能预测   机器学习   多示例多标记学习  

摘要： 介绍了蛋白质功能预测与分析的现状和主要研究内容,并说明了如何利用新型机器学习方法来进行这方面的研究工作,并对其以后的发展提出了展望。

关键词： 事件指代消歧   机器学习方法   实例生成   特征选择  

摘要： 与实体指代不同,事件指代其先行词候选是一个事件,与名词性的指代词具有完全不同的语义分类体系,因此适用于实体指代消歧的大多数特征都不能用于事件指代消歧。给出了一个基于机器学习方法的事件指代消歧平台,详细介绍了平台的实例生成和特征选择过程,给出了平台在OntoNotes3.0语料上的事件指代消歧的结果,并对结果进行了分析。从实验结果可以看到,给出的平台获得了较好的召回率,但系统准确率需要进一步提升。

关键词： Hydrocarbons   Molecular modeling   Molecules   Organic compounds   Viscosity  

摘要： In the present work, temperature dependent models for the prediction of densities and dynamic viscosities of pure compounds within the range of possible alternative fuel mixture components are presented. The proposed models have been derived using machine learning methods including Artificial Neural Networks and Support Vector Machines. Experimental data used to train and validate the models was extracted from the DIPPR database. A comparison between models using an ample range of molecular descriptors and models using only functional group count descriptors as inputs was performed, and consensus models were created by testing different combinations of the individual models. The resulting consensus models' predictions were in agreement with the available experimental data. Comparisons were also made between predictions of our models and correlations validated by the DIPPR staff. Our models were used to predict densities and dynamic viscosities of compounds for which no experimental data exists. Our models were also used to estimate other properties such as kinematic viscosities, critical temperatures, and critical pressures for compounds in the database. Finally, predictions were used to study the main trends of density and viscosity at the aforementioned temperatures as a function of the number of carbon atoms for chemical families of interest.