关键词： 系统要求   医疗文本分类   机器学习算法  

摘要： 为了解决看病难、看病贵的问题,在人工智能的指导下,研发出远程医疗机器人智能导诊系统。其中,机器学习算法扮演着重要的角色,它相当于医生的大脑。本文将对医疗领域常用的机器学习方法进行研究,包括:决策树、神经网络、贝叶斯以及k邻近方法等。通过比较各种机器学习算法在医疗领域的分类效果,发现朴素贝叶斯算法用于疾病诊断算法的效果更好。

关键词： 数据预测   神经网络   R平台  

摘要： 大数据的发展对于数据挖据既是挑战,更是机遇。数据挖掘具有解决数据内在结构和发现数据预测的作用,神经网络用来指示一类广泛的模型和算法,基于一些观测数据的集合生成隐藏的神经元。R语言是一个开源、跨平台的编程语言,现已成为数据分析领域标志性语言。本研究依托R平台,设计人工神经网络的建立步骤,构建R的神经网络实现、应用并且进行结果解读,运用神经网络实现数据的分类预测。

关键词： NFV场景   智能算法   大数据   智能化维护平台  

摘要： 主要介绍了基于机器学习的日志分析应用于NFV场景下的故障快速定界定位系统的设计与实现。该系统主要基于大数据分析、人工智能算法、云计算基础设施构建了一个智能化的维护平台,将IT技术的优势应用于CT运维领域,同时继承了CT运维领域故障经验库和规则的知识积累,结合告警和性能指标信息,最大化地实现故障根因的快速定界定位和业务快速恢复。

关键词： 信用卡欺诈行为   机器学习   比较研究  

摘要： 近年来,信用卡发卡量的大幅度增长也带来了全球信用卡欺诈交易数目的与日俱增,欺诈方法不断更新,作案技巧日益精明。过往的以人力识别为主、机器识别为辅的信用卡欺诈识别机制已经不再满足当今信用卡欺诈识别的需求,如何有效、快速、准确地识别出信用卡欺诈交易已成为目前银行界一个亟待解决的问题。文章对近期信用卡欺诈行为识别的相关文献进行综述。既有文献将神经网络、决策树、随机森林等机器学习方法应用于信用卡欺诈问题,证明机器学习模型对于信用卡欺诈问题解决的有效性。

关键词： 音乐数据   分类算法   信念网络   情绪   计算机技术   音乐特征识别   卷积神经网络   机器学习方法  

摘要： 随着娱乐媒体行业的发展,音乐已成为了人们生活中必不可少的一部分,但是面对海量的音乐数据,如何根据不同环境以及用户的不同心情推荐出合适的音乐已成为近些年研究的热点问题。在此背景下,基于情绪的音乐分类任务受到了越来越多的关注。音乐是抒发人类生活情感的一种艺术,因此可以通过计算机技术提取音乐特征识别音乐包含的情绪。本技术基于深度信念网络,结合卷积神经网络提出了一种音乐情绪分类算法。实验证明,该方法在音乐情绪分类任务上较传统机器学习方法和原始深度信念网络都有较好的效果提升。

关键词： 数据挖掘   CART算法   降水日数  

摘要： 夏季降水日数的准确预测,对于保障农业、运输业、电力等行业的有序进行具有重要现实意义.利用连云港市气象局提供的1951—2012年夏季降水数据对连云港地区的降水日数特征进行分析,难以直观地发现夏季降水日数随时间分布的规律.为进一步探索降水日数的发生规律,结合国家气候中心网站提供的多种气候因子数据,基于CART决策树算法构建了连云港地区夏季降水日数是否偏多与是否偏少的分类与预测模型.该模型可以发现在多种气候因子不同条件下,夏季降水日数是否偏多(偏少)的规律,模型的分类与预测都具有良好的效果.利用52 a的数据样本训练模型,模型的训练准确率为90. 38%(86. 54%),再用剩余10 a数据样本检验模型,测试准确率为80%(80%),并且得到规则集,方便气象业务人员使用以及决策服务人员参考.同时,为降水日数的预测提供了数据挖掘的新思路.

关键词： 贝叶斯分类算法   决策支持   生源预测  

摘要： 高职教师在教学过程中为了更好地完成教学任务,运用多种教学方法和教学模式。为了更好地实现科学教学,符合当前互联网与大数据高速发展的时代背景,雨课堂出现并逐步融入到高职教育的课堂中。本文从雨课堂的基本功能入手,提出教师在使用雨课堂进行教学过程中所面临的挑战以及应对的具体措施。旨在为使用雨课堂授课的教师带来一些意见和建议,让使用雨课堂进行教学的教师在享受便利的同时也能够看清挑战,进而提高自身教育教学能力、采取最合适有效的策略,创造出更加精彩的课堂。

关键词： 5G networks   Coverage maps   Machine learning   Radial basis networks   Multi-layer perceptron  

摘要： Accurate assessment of the wireless coverage of a station is considered a key feature in 5G networks. Determining the reception coverage of transmitters becomes a complicated problem when there are interfering transmitters, and it becomes increasingly more complicated when the transmission powers of those transmitters are not uniform. In this paper, we compare different Machine Learning techniques that can be used to predict the wireless coverage maps. We consider the following Machine Learning methods: (1) Radial Basis Network;a type of Artificial Neural Network which typically uses Gaussian kernels, (2) an Artificial Neural Network which uses a sigmoid function as an activator,(3) A Multi-Layer Perceptron with two hidden layers, and (4) the K-Nearest-Neighbors technique. We show how it is possible to train the Neural Networks to generate coverage maps based on samples and we check the accuracy level of the learning process on a test set, using these four different learning techniques. The conclusion of our experiments is that if the sample points are randomly located, the Radial Basis Network and the Multi-Layer Perceptron perform better than the other methods. Thus, these models can be considered promising candidates for learning coverage maps, and can be used for efficient spectrum management within the framework of 5G cellular networks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： high-frequency trading   limit order book   mid-price   machine learning   ridge regression   single hidden feedforward neural network  

摘要： Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high-frequency limit order markets for mid-price prediction. We extracted normalized data representations of time series data for five stocks from the Nasdaq Nordic stock market for a time period of 10 consecutive days, leading to a dataset of similar to 4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large-scale dataset can serve as a testbed for devising novel solutions of expert systems for high-frequency limit order book data analysis.

关键词： Massive MIMO   localization   machine learning (ML)   Gaussian process (GP) regression   Cramer-Rao bounds  

摘要： We propose a supervised machine learning (ML) approach based on Gaussian process (GP) regression to position users in a distributed massive multiple-input multiple-output (DM-MIMO) system from their uplink received signal strength (RSS). The proposed approach serves as a proof-of-concept that we can localize users by training an ML model with noise-free RSS and using the trained model to estimate the test user locations from their noisy RSS. We consider two GP methods for localization, namely, the conventional GP (CGP) and the numerical approximation GP (NaGP). We find that the CGP provides unrealistically small 2 sigma error-bars on the location estimates. Therefore, we derive the true predictive distribution and employ NaGP to obtain realistic 2 sigma error-bars on the location estimates. Next, we derive a Bayesian Cramer-Rao lower hound (BCRLB) on the root-mean-squared-error (RMSE) performance of the two GP methods. Numerical studies reveal that: 1) the NaGP indeed provides realistic 2 sigma error-bars on the estimated locations;2) both the CGP and NaGP achieve RMSEs that are close to the BCRLBs;3) the presence of correlated shadowing improves the RMSE performance;and 4) extrapolation to the zero input noise scenario can significantly improve the RMSE achieved by the NaGP.