关键词： Sports  

摘要： Sports prediction is an important part of sports statistics. This paper presents an efficient and robust automatic process for large-scale sports data analysis. With the development of interdisciplinary integration, such as the computing technology and the gray system, etc, there are some flaws in the scopes of its theory exploring, applied research and methods. Using improved rough neural network and danger model immune algorithm, the essay analyzed the Large-scale Sports Data. The significance of this framework is its automatic characteristic in testing with minimum human involvement in training, as well as the scalability and expansibility in dealing with a large-scale dataset. Overall, this framework demonstrates the efficiency and accuracy in processing voluminous data from sports collection and achieves various tasks in video analysis. It also demonstrates a potential technology transformation from the "laboratory bench" to commercial applications.

关键词： Grid computing   Wait time prediction   Machine learning  

摘要： To have high performance scheduling mechanisms in grid computing, we need accurate methods for estimating parameters like jobs' wait time and run time. In this paper, we consider wait time prediction problem. Different regression techniques are examined on AuverGrid data set to predict wait time. To improve the quality of prediction, some extra features are proposed. Simulation results show that adding these features reduces prediction error between 13% and 60% in different methods. Results also show that K-nearest neighbor outperforms other regression techniques. We have compared the k-nearest neighbor method in both original and enriched data set with Last-M. K-Nearest neighbor in enriched data set outperforms both Last-M and K-nearest neighbor in original data set in accuracy and perfect prediction percentage.

关键词： Contextual Feature   Text Source   Name Entity Recognition   Machine Learning Classifier   Probabilistic Machine  

摘要： Background: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. Results: We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9% and recall = 70.5%) compared to a popular dictionary based approach (precision = 97.5% and recall = 54.3%) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central's full text articles annotated with scientific names, the precision and recall values are 98.5% and 96.2% respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Conclusions: We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://***.

关键词： Machine learning   Gene expression   Gene regulation   Genetic loci   Gene mapping   Genetic networks   Genetic polymorphism   Machine learning algorithms  

摘要： Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.

摘要： Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

关键词： Tandem Mass Spectrum   Group Node   Quality Spectrum   Constraint Optimization Problem   Tandem Spectrum  

摘要： Background: In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets. Results: This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56% and 62% of database searching time while losing only a small amount of high-quality spectra. Conclusions: Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.

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。