基于改进极限学习机的中长期电力负荷预测

根据神经网络的非线性辨识能力和极限学习机(ELM)的高计算速度、高泛化能力等特点,提出一种批处理和逐次迭代相结合的改进极限学习机方法。实验结果表明,改进的极限学习机方法应用于中长期的电力负荷预测中,比传统的极限学习机效果更优。     

网络学校

网络学校的构造大概如下:每一所学校就是一个总的网络系统,分有年级、班级以便层层管理。发给每一位老师、学生一台网络学习机。班主任主要通过网络观察自己班里每位学生的学习情况。任课老师按课本知识讲课(通过操作学习机进行)、批改作业等。学生认真、自觉地到学习机前学习。家长监督学生的作息情况。政府保护好网络安全,监督学校制度等等。注:学生学习在家中,不用每天到学校上课、用本子做作业。学生考上某所学校后,原来的学校会将此学生的网络信号转到考上的学校,并根据学生的表现发给毕业证书等。益处:节约了学生所用的一切学习工具耗…     

基于混合核函数的极限学习机遥感图像分类

针对遥感影像数据集庞大,地物复杂难辨等特性导致分类难度加大的问题,文中构建了一种基于混合核函数极限学习机的遥感图像分类方法。运用该方法对遥感图像数据集进行分类处理,并将其与单核极限学习机、无核极限学习机、支持向量机等方法进行了对比。实验结果表明,基于混合核函数的极限学习机在对遥感图像进行分类时,其总体精度更优,且一致性效果更好。     

多核极限学习机算法仿真实证研究

给出了标准极限学习机的回归模型,根据现有单核极限学习机回归能力的不足,提出了多核极限学习机,对多个不同的核函数根据实际情况进行加权组合,充分利用不同核函数的特有优势,对所给的样本进行充分分析,进而提高函数的逼近精度,通过仿真实例证实了所提方法的有效性。     

AdaBoost算法在人脸识别中的应用

AdaBoost算法是在Boosting算法的基础上的改进,具有自适应性,目的是通过建立多学习机的组合使得弱学习机的性能得到提升成为强学习机,它具有对过学习现象的免疫性,近年来成为研究热点。本文探讨如何利用AdaBoost方法进行人脸的检测。     

基于极速学习机的服装搭配智能推荐系统设计

随着电子商务的发展,越来越多的人选择网上购物。针对用户尤其是女性用户网上购买服装的个性化需求,提出了基于极速学习机的服装搭配智能推荐系统。该系统根据用户自身的特征(身高、体重、胸围、腰围、臀围和脚码)及用户选择的条件(风格、场合、季节和颜色),利用极速学习机算法,自动为用户推荐个性化的服装搭配方案。实验结果表明,基于极速学习机的服装搭配智能推荐系统能够为用户提供满意的服装搭配方案,具有一定的实用价值。     

为盲人造福是我的责任——访电子盲文学习机发明者励印龙

2007年10月31日,国家知识产权局授予励印龙、刘海霞研发的语言提示电子盲文教具(即盲文学习机)专利权,专利号ZL200620149027.8;2009年1月4日,在中国盲协召开的纪念布莱尔诞辰200周年座谈会上,励印龙拿出了已研制成功的"盲文学习机",引起与会人士的关注;2009年8月27日,在宁夏银川捐赠会上,励印龙、刘海霞、励金升当场向宁夏残联捐赠500台盲文学习机,向宁夏各族盲人献出一片爱心……励印龙是河北景县退休干部,他当过兵,做过县广播电视局局长,是一个非职务发明者,至今已有20多项发明专利,他如何与盲人相识相交,如何走进盲人群体,为何热心研发盲文学习机?为此,《盲人月刊》记者专访了励印龙。     

轻轻松松学电脑

在电脑普及方面,专为学生设计的电脑学习机已进入市场。这些学习机具备掌握电脑知识和学习功课的功能,并配有独特的语音系统,能够发出标准的汉、英语语音,很适合孩子们学习。从严格意义上讲,这些学生学习机虽然还不属真正的电脑,但就它的基本功能及操作来说,既可为学生掌握基本的电脑知识打下基础,又可充当高水平电脑的媒介,使人们尤其是孩子     

模拟电路故障诊断

本文针对模拟电路的故障诊断和检测技术方面的问题进行研究,提出了一种应用极限学习机原理的电路故障检测方法,阐述了支持向量机原理、ELM极限学习机原理、ELM故障诊断方法。将极限学习机应用到模拟电路故障诊断中,计算算法更加简单有效、具有更快的学习速度,以及其他诸多优良特性。本设计提出的模拟电路故障诊断方法,研究了系统的诊断方法。将故障诊断方法进行电路仿真实验分析,实验结果表明这些方法的有效性,能较好地分析和检测模拟电路故障,弥补了传统检测方法的不足,为实际工程问题的解决提供了一种新思路。     

装在兜里的多媒体教材

作为掌上电脑的一个特殊类型,电子词典一直牢牢的占领着学校这个市场,实际上,各个电子词典品牌之间的竞争非常激烈,一些功能日趋丰富、早已超越词典范畴的学习机在市场上也越来越多了。比如这款好易通影音直通车9188学习机。     

Autonomic machine learning platform

Acquiring information properly through machine learning requires familiarity with the available algorithms and understanding how they work and how to address the given problem in the best possible way. However, even for machine-learning experts in specific industrial fields, in order to predict and acquire information properly in different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the application of machine learning. For non-experts, it is much more difficult to make accurate predictions through machine learning.In this paper, we propose an autonomic machine learning platform which provides the decision factors to be made during the developing of machine learning applications. In the proposed autonomic machine learning platform, machine learning processes are automated based on the specification of autonomic levels. This autonomic machine learning platform can be used to derive a high-quality learning result by minimizing experts’ interventions and reducing the number of design selections that require expert knowledge and intuition. We also demonstrate that the proposed autonomic machine learning platform is suitable for smart cities which typically require considerable amounts of security sensitive information.     

基于机器学习的自适应个性化数字图书馆模型研究

由于信息数量和种类增加,用户对数字图书馆期待更多的智能服务.本文提出通过引入机器学习技术来解决此问题,首先简要介绍了机器学习技术,说明了可适应个性化数字图书馆局限性,然后提出基于机器学习的自适应个性化数字图书馆模型,最后探讨了用户模型的自动创建.实践表明,此模型可满足用户对信息的需要,简化信息查找过程.     

智能教学系统的自学习机制

本文主要介绍了智能教学系统中的机器自学习机制,研究如何提高智能教学系统的智能性和通用性等方面的问题。文章采用基于信息论的示例学习,改进了决策树学习算法,并建立了机器学习决策树。     

Exploring human resource management intelligence practices using machine learning models

The use of machine learning for recruitment has become one of the main themes in human resources ever since machine learning software investigated the first recruitment software and discovered that utilizing technology improves their effectiveness at work, speed, and makes the process simpler. In order to better handle employee files, profiles, turnover, data analytics, and the creation of electronic personal data sheets for government service records, a human resource information system that incorporates machine learning has been created. Using a supervised machine learning technique, it was designed to foresee staff turnover. From a theoretical perspective, machine learning apps may be able to perform the same tasks as HR specialists, if not better or faster. Supporting HR professionals in becoming a true business partner and providing them with accurate and reliable advice, the interaction between HR professionals and line top management believes that the HR professionals still has surplus over machine learning, alone. Human resources methods and the significance of machine learning are the primary focus of this paper. This paper's three goals are to (1) determine how much of an impact Machine learning can have on the organization's recruitment procedures, (2) examine the extent to which this technology has been adopted, and (3) examine the frequency with which complaints have been lodged during these crucial exercises.     

基于机器学习算法的研究热点趋势预测模型对比与分析——BP神经网络、支持向量机与LSTM模型

[目的/意义]细粒度分析学科领域热点主题发展脉络并对利用机器学习算法对未来发展趋势进行准确预测研究。[方法/过程]提出一种基于机器学习算法的研究热点趋势预测方法与分析框架，以基因工程领域为例利用主题概率模型识别WOS核心集中论文摘要数据研究热点主题并进行主题演化关联构建，然后选取BP神经网络、支持向量机及LSTM模型等3种典型机器学习算法进行预测分析，最后利用RE指标和精准度指标评价机器学习算法预测效果并对基因工程领域在医药卫生、农业食品等方面研究趋势进行分析。[结果/结论]实验表明基于LSTM模型对热点主题未来发展趋势预测准确度最高，支持向量机预测效果次之，BP神经网络预测效果较差且预测稳定性不足，同时结合专家咨询和文献调研表明本文方法可快速识别基因领域研究主题及发展趋势，可为我国学科领域大势研判和架构调整提供决策支持和参考。     

Semi-supervised document retrieval

This paper proposes a new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRank, aims to use the advantages of both the traditional Information Retrieval (IR) methods and the supervised learning methods for IR proposed recently. The advantages include the use of limited amount of labeled data and rich model representation. To do so, the method adopts a semi-supervised learning framework in ranking model construction. Specifically, given a small number of labeled documents with respect to some queries, the method effectively labels the unlabeled documents for the queries. It then uses all the labeled data to train a machine learning model (in our case, Neural Network). In the data labeling, the method also makes use of a traditional IR model (in our case, BM25). A stopping criterion based on machine learning theory is given for the data labeling process. Experimental results on three benchmark datasets and one web search dataset indicate that SSRank consistently and almost always significantly outperforms the baseline methods (unsupervised and supervised learning methods), given the same amount of labeled data. This is because SSRank can effectively leverage the use of unlabeled data in learning.     

关于支持向量回归机的模型选择

支持向量机是在统计学习理论基础上发展起来的一种新型的机器学习方法。模型选择是设计支持向量机的重要内容之一。本文在分析用于回归的支持向量机原理的基础上,分别从核函数的选择、模型参数的作用、模型参数的调整方法等模型选择方面进行了综述,并讨论了模型选择的优缺点,最后指出在实际应用中常见的核函数和模型参数调整方法。     

Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation extraction. In this research, we studied the paper section identification problem in the context of Chinese medical literature analysis, where the subjects, methods, and results are more valuable from a physician's perspective. Based on previous studies on English literature section identification, we experiment with the effective features to use with classic machine learning algorithms to tackle the problem. It is found that Conditional Random Fields, which consider sentence interdependency, is more effective in combining different feature sets, such as bag-of-words, part-of-speech, and headings, for Chinese literature section identification. Moreover, we find that classic machine learning algorithms are more effective than generic deep learning models for this problem. Based on these observations, we design a novel deep learning model, the Structural Bidirectional Long Short-Term Memory (SLSTM) model, which models word and sentence interdependency together with the contextual information. Experiments on a human-curated asthma literature dataset show that our approach outperforms the traditional machine learning methods and other deep learning methods and achieves close to 90% precision and recall in the task. The model shows good potential for use in other text mining tasks. The research has significant methodological and practical implications.     

Text classification from unlabeled documents with bootstrapping and feature projection techniques

Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, generally known as supervised learning. However, the supervised learning approaches have some problems. The most notable problem is that they require a large number of labeled training documents for accurate learning. While unlabeled documents are easily collected and plentiful, labeled documents are difficultly generated because a labeling task must be done by human developers. In this paper, we propose a new text classification method based on unsupervised or semi-supervised learning. The proposed method launches text classification tasks with only unlabeled documents and the title word of each category for learning, and then it automatically learns text classifier by using bootstrapping and feature projection techniques. The results of experiments showed that the proposed method achieved reasonably useful performance compared to a supervised method. If the proposed method is used in a text classification task, building text classification systems will become significantly faster and less expensive.     

融合分治法与支持向量机的“算法设计与分析”教学模式研究

"算法设计与分析"是计算机类本科生的专业必修课,内容涵盖递归、分治等多种算法的模型设计、代码实现和案例分析。有效掌握课程内容对日后从事机器学习方向的算法工程师岗位或进一步地科研深造等均具有十分重要的作用。然而,当前的课程内容缺乏与相关机器学习算法的关联性分析,导致学生难以将课程所学知识有效运用在实际应用或科研工作中。为此,本文以分治法为例,探讨将分治法的求解过程与运用支持向量机求解多类分类问题的一对一方法有效融合在一起。通过分析新的课程教学模式,进一步培养学生日后从事相关工作的能力。