关键词： 手写数字识别   机器学习   SVM   人工神经网络  

摘要： 手写数字识别是光学数字识别技术的一个重要分支,本质上是图片分类问题,利用机器学习的方法解决手写数字识别问题已经达到非常高的精度。本文主要介绍了手写数字识别的背景、国内外研究现状、手写数字识别的一般步骤。文章第三分部对手写数字识别步骤做了重点介绍,包括支持向量机算法和人工神经网络算法。

关键词： 文本分类   机器学习   中文分词   特征向量提取  

摘要： 文本分类是处理和组织文本信息的关键技术,能够帮助有效地组织信息,快速区分有效信息和无用信息,满足用户的个性化需求。本文主要介绍了文本分类的背景、国内外的研究现状以及利用机器学习方法解决文本分类问题的一般步骤。文章第二部分对中文分词、特征向量提取、分类器训练和评估原理做了重点介绍,包括了自然语言处理的统计语言模型,机器学习的KNN、SVM、神经网络算法。

关键词： 机器学习   药物不良反应   多任务学习   多核函数学习   伽玛泊松衰减   多变量线性回归   相似度计算  

摘要： 目前,医疗安全正日益得到重视。其中,对药物不良反应信号的预测,在药物安全研究领域与新药研发领域具有重要的意义。药物不良反应每年会造成全球三分之一的医疗事故,以及上千亿美元的经济损失。因此,对药物不良反应的研究越来越受到世界各国的重视。为了监测药物不良反应,学者们基于医疗数据库提出了一些统计学模型和数据挖掘方法来监测/预测<药物-不良反应>关联关系。一方面,相关的数据挖据方法大都是基于关联规则或模式匹配算法,挖掘<药物-不良反应>的关联关系。由于这些数据挖掘算法只关注高频繁度的关联关系,因此存在两类缺点:(1)无法监测到低频的关联关系;(2)由于忽略了关联规则中混杂因子的影响,因此无法控制伴随药物对监测关联关系的影响,从而造成较高的错误率。另一方面,统计学对药物不良反应问题的建模,大都在小样本数据上基于列关联表计算<药物,不良反应>关联关系的强度,因此存在两类缺点:(1)小样本数据造成关联关系预测信号的偏差较大;(2)无法基于不同病人进行个性化预测。为解决上述问题,本文通过收集处理海量医疗数据,并基于机器学习方法针对药物不良反应监测的相关问题进行了研究。首先,本文的第一个研究问题是病人药物不良反应的个性化预测问题。药物不良反应的个性化,是指由于病人体质的差异,相同药物在不同病人体内会产生特定的不良反应,因此需要根据不同病人的特征来预测相应的药物不良反应。本文提出了一种基于病人特征相似度计算的多任务学习模型。该工作基于美国食品药品管理局公开的药物不良反应数据库FAERS,对病人信息构建特征空间,并首次提出将推荐系统中个性化推荐的方法应用在药物安全研究领域。本文基于FAERS数据提出多任务学习模型,通过计算病人与各种不良反应的关联强度,建立对应的个性化药物不良反应排序表。此外,本文原创性提出了一种新的衡量药物不良反应关联强度的验证标准HitRate@nn。通过实验表明,该模型在预测病人个性化药物不良反应问题上,有较高的准确率。其次,本文的第二个研究问题是药物多频率不良反应的预测问题。由于不同频率的不良反应造成的问题和危害不同,尤其是低频药物不良反应在临床测试阶段很难被检测出来,因此需要根据不同病人特征及药物属性来预测不同频率的不良反应。为此,本文提出了一种基于多核函数学习的多任务学习模型。该工作通过分析FAERS结构化数据,提出根据药物分子结构差异进行特征分类,并构建多核函数池。该模型通过核函数学习方法找出每一类特征对应的最优核函数,并根据不同特征构建对应的凸优化限制条件以及规则化函数,令相同特征在不同的病人不良反应预测任务中实现权重自动调整,达到预测不同频率不良反应的要求。同时,该模型将基于历史任务学习中训练的药物特征权重及不同药物特征核函数间的关系权重,用于对病人新药组合的预测任务中,可以实现对病人新药组合不同频率不良反应的预测。在验证标准HitRate@n的基础上,本文提出了一种衡量不同频率不良反应的验证标准overall-HitRate@n。实验表明,该模型在预测病人不同频率不良反应的问题上,尤其是低频药物不良反应,均优于所比较的方法。最后,本文的第三个研究问题是计算<药物,不良反应>关联关系的正阳性/因果性问题。由于小样本数据以及混杂因子(即伴随药物)的影响,造成所监测的关联关系的准确率较低,即关联关系的正阳性/因果性较弱。本文基于收集处理的4百万条FAERS病人数据,提出了一种伽玛泊松衰减多变量线性回归模型。本文使用伽玛泊松共轭先验分布对<药物,不良反应>的出现频率建模,并提出在监测单一药物不良反应关联关系时,将特征空间中其他所有药物视作伴随药物,通过建立回归模型减少混杂因子对关联关系的影响。本文对药物不良反应监测相关问题进行了深入研究,针对特定问题,给出了针对性的解决方案。本文的创新点和贡献如下:(1)针对药物不良反应个性化预测问题,本文首次提出了一种基于病人特征相似度计算的多任务学习模型;(2)针对数据挖掘方法难以监测低频药物不良反应的问题,本文提出基于多核函数多任务学习模型,通过多核函数多任务凸优化学习来预测药物不同频率的不良反应,并能有效的监测到低频药物的不良反应;(3)针对<药物,不良反应>关联关系正阳性/因果性的准确率问题,本文提出一种基于伽玛泊松衰减多变量线性回归模型,通过控制混杂因子来提高预测<药物,不良反应>关联关系的正阳性/因果性。综上所述,本文在基础理论和关键技术方面的研究成果为监测药物不良反应问题提供了新的途径。

关键词： Accelerometers   Algebra   Algorithms   Amniotes   Animal behavior   Animals   Applied mathematics   Artificial intelligence   Behavior   Biology and life sciences   Cognitive science   Computer and information sciences   Earth sciences   Electronics   Engineering and technology   Foraging   Machine learning   Machine learning algorithms   Mammals   Marine and aquatic sciences   Marine biology   Marine mammals   Mathematics   Neuroscience   Organisms   Physical sciences   Polynomials   Research and analysis methods   Research Article   Sea lions   Seals   Simulation and modeling   Vertebrates   Zoology  

摘要： Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers. From video we identified 26 behaviours that could be grouped into one of four categories (foraging, resting, travelling and grooming) representing key behaviour states for wild seals. We used data from 10 seals to train four predictive classification models: stochastic gradient boosting (GBM), random forests, support vector machine using four different kernels and a baseline model: penalised logistic regression. We then took the best parameters from each model and cross-validated the results on the two seals unseen so far. We also investigated the influence of feature statistics (describing some characteristic of the seal), testing the models both with and without these. Cross-validation accuracies were lower than training accuracy, but the SVM with a polynomial kernel was still able to classify seal behaviour with high accuracy (>70%). Adding feature statistics improved accuracies across all models tested. Most categories of behaviour-resting, grooming and feeding were all predicted with reasonable accuracy (52-81%) by the SVM while travelling was poorly categorised (31-41%). These results show that model selection is important when classifying behaviour and that by using animal charact

摘要： The Dishevelled/EGL-10/Pleckstrin (DEP) domain-containing (DEPDC) proteins have seven members. However, whether this superfamily can be distinguished from other proteins based only on the amino acid sequences, remains unknown. Here, we describe a computational method to segregate DEPDCs and non-DEPDCs. First, we examined the Pfam numbers of the known DEPDCs and used the longest sequences for each Pfam to construct a phylogenetic tree. Subsequently, we extracted 188-dimensional (188D) and 20D features of DEPDCs and non-DEPDCs and classified them with random forest classifier. We also mined the motifs of human DEPDCs to find the related domains. Finally, we designed experimental verification methods of human DEPDC expression at the mRNA level in hepatocellular carcinoma (HCC) and adjacent normal tissues. The phylogenetic analysis showed that the DEPDCs superfamily can be divided into three clusters. Moreover, the 188D and 20D features can both be used to effectively distinguish the two protein types. Motif analysis revealed that the DEP and RhoGAP domain was common in human DEPDCs, human HCC and the adjacent tissues that widely expressed DEPDCs. However, their regulation was not identical. In conclusion, we successfully constructed a binary classifier for DEPDCs and experimentally verified their expression in human HCC tissues.

关键词： ocean   eddy   machine learning   eddy lifetime   remote sensing  

摘要： We report a novel altimetry-based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency.

关键词： Ferry traffic   Traffic prediction   Machine learning   Periodic analysis   Algorithm composition  

摘要： Ferry service plays an important role in several cities with waterfront areas. Transportation authorities often need to forecast volumes of vehicular traffic in queues waiting to board ships at ferry terminals to ensure sufficient capacity and establish schedules that meet demand. Several previous studies have developed models for long-term vehicle queue length prediction at ferry terminals using terminal operation data. Few studies, however, have been undertaken for short-term vehicular queue length prediction. In this study, machine learning methods including the artificial neural network (ANN) and support vector machine (SVM) are applied to predict vehicle waiting queue lengths at ferry terminals. Through time series analysis, the existence of a periodic queue-length pattern is established. Hence, methodologies used in this study take into account periodic features of vehicle queue data at terminals for prediction. To further consider the cyclical characteristics of vehicle queue data at ferry terminals, a prediction approach is proposed to decompose vehicle waiting queue length into two components: a periodic part and a dynamic part. A trigonometric regression function is introduced to capture the periodic component, and the dynamic part is modeled by SVM and ANN models. Moreover, an assembly technique for combining SVM and ANN models is proposed to aggregate multiple prediction models and in turn achieve better results than could be attained from a lone predictive method. The prediction results suggest that for multi-step ahead vehicle queue length prediction at ferry terminals, the ensemble model outperforms the separate prediction models and the hybrid models, especially as prediction step size increases. This research has important practical significance to both traffic service management interests and the travelers in cities along waterfront areas.

关键词： Materials informatics   Density functional theory   Regression  

摘要： The design of new and improved materials for different applications of interest is boosted by combining computations or experiments with machine learning techniques. Materials scientists seek to use learning algorithms that can easily and efficiently be applied to their data in order to obtain quantitative property prediction models. Here, we utilize a first principles generated dataset of the electronic and dielectric properties of a chemical space of polymers to test different kinds of regression algorithms used by the machine learning community today. We explore several possibilities for the hyper-parameters that go into such learning schemes, and establish optimal strategies and parameters for high-fidelity polymer dielectrics property prediction models. Published by Elsevier B.V.

关键词： Machine learning   Malware detection   Static analysis   Structural features   Microsoft office open xml   Document  

摘要： Office documents are used extensively by individuals and organizations. Most users consider these documents safe for use. Unfortunately, Office documents can contain malicious components and perform harmful operations. Attackers increasingly take advantage of naive users and leverage Office documents in order to launch sophisticated advanced persistent threat (APT) and ransomware attacks. Recently, targeted cyber-attacks against organizations have been initiated with emails containing malicious attachments. Since most email servers do not allow the attachment of executable files to emails, attackers prefer to use of non-executable files (e.g., documents) for malicious purposes. Existing anti-virus engines primarily use signature-based detection methods, and therefore fail to detect new unknown malicious code which has been embedded in an Office document. Machine learning methods have been shown to be effective at detecting known and unknown malware in various domains, however, to the best of our knowledge, machine learning methods have not been used for the detection of malicious XML-based Office documents (*.docx, *.xlsx, *.pptx, *.odt, *.ods, etc.). In this paper we present a novel structural feature extraction methodology (SFEM) for XML-based Office documents. SFEM extracts discriminative features from documents, based on their structure. We leveraged SFEM's features with machine learning algorithms for effective detection of malicious *.docx documents. We extensively evaluated SFEM with machine learning classifiers using a representative collection (16,938 *.docx documents collected "from the wild") which contains 4.9% malicious and similar to 95.1% benign documents. We examined 1,600 unique configurations based on different combinations of feature extraction, feature selection, feature representation, top-feature selection methods, and machine learning classifiers. The results show that machine learning algorithms trained on features provided by SFEM successful

关键词： Random Forest   Eicosanoid   Ultra Performance Liquid Chromatography   Random Forest Model   Relative Liver Weight  

摘要： Background: The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-alpha, PPAR-gamma, and PPAR-d. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-alpha, PPAR-gamma, and -delta) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry. Results: In order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content. Conclusions: We found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transamin