Improved customer choice predictions using ensemble methods

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression.     

Solitary wave solutions of nonlinear financial markets :data-modeling-concept-practicing

This paper seeks to solve the difficult nonlinear problem in financial markets on the complex system theory and the nonlinear dynamics principle, with the data-model-concept-practice issue-oriented reconstruction of the phase space by the high frequency trade data. In theory, we have achieved the differentiable manifold geometry configuration, discovered the Yang-Mills functional in financial markets, obtained a meaningful conserved quantity through corresponding space-time non-Abel localization gauge symmetry transformation, and derived the financial solitons, which shows that there is a strict symmetry between manifold fiber bundle and guage field in financial markets. In practical applications of financial markets, we have repeatedly carried out experimental tests in a fluctuant evolvement, directly simulating and validating the existence of solitons by researching the price fluctuations (society phenomena) using the same methods and criterion as in natural science and in actual trade to test the stock Guangzhou Proprietary and the futures Fuel Oil in China. The results demonstrate that the financial solitons discovered indicates that there is a kind of new substance and form of energy existing in financial trade markets, which likely indicates a new science paradigm in the economy and society domains beyond physics.      

F150交换机告警关联规则的研究

为了提高电信网络运营效率，降低维护成本，电信运营企业在网络告警管理系统中需要引入数据挖掘技术。本文主要研究利用数据挖掘技术来进行故障的管理，即对告警进行关联性分析，详细分析了告警序列数据的关联规则挖掘算法，并在南昌本地网F150交换机的告警数据库中予以实现，同时对实际挖掘结果进行分析和整理，推导出一些实用的关联规则。     

基于Web管理技术的安全网络管理系统

分析了基于Web的网络管理技术，提出了一种解决网络管理安全的方法。     

ASP+ADO实现医院门诊在线查询

ASP技术由于其方便 ,灵活及可扩充性等特点和简明、高效的技术优势 ,在Web数据库的开发中获得了广泛的应用。简述了开发Web数据库的重要性 ,介绍了ASP技术的特点及其数据库访问原理 ,ADO对象。实例介绍了ASP实现医院门诊在线查询的方法     

Reformulation of the support set selection problem in the logical analysis of data

The paper is concerned with the problem of binary classification of data records, given an already classified training set of records. Among the various approaches to the problem, the methodology of the logical analysis of data (LAD) is considered. Such approach is based on discrete mathematics, with special emphasis on Boolean functions. With respect to the standard LAD procedure, enhancements based on probability considerations are presented. In particular, the problem of the selection of the optimal support set is formulated as a weighted set covering problem. Testable statistical hypothesis are used. Accuracy of the modified LAD procedure is compared to that of the standard LAD procedure on datasets of the UCI repository. Encouraging results are obtained and discussed.     

全球信息网WWW上的化学资源

文献查阅是化学工作者必须掌握的基本技能之一.随着计算机网络技术的迅速发展,利用全球信息网WWW浏览和获取化学信息已成为查阅化学文献最重要的方法之一.在化学工作者中普及万维网WWW查阅各类化学信息和获取化学资源的方法是非常重要的.作者简要叙述WWW的基本原理和通过WWW获取化学信息和化学资源的方法,并给出部分Internet化学资源.     

Deep ensemble-based hard sample mining for food recognition

Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics.     

面向智慧能源的供需平衡数据可视化挖掘方法

为满足能源数据监测和综合分析领域的业务需要,提出面向智慧能源的供需平衡数据可视化挖掘方法。构造数据迁移与分类视图,确定每个供需平衡数据点位置坐标,并采用正交投影可视化技术将数据挖掘算法核心结构展示在视图中(支持用户对其进行相应调整);引入数据挖掘算法,提取数据可视化挖掘结果参数,实现面向智慧能源供需平衡数据的可视化挖掘。实验结果显示:智慧能源供需数据可视化挖掘结果综合评分数值范围为64.70~82.04分,充分说明该方法具备较好的可视化挖掘性能,可应用在多维典型用能场景中。     

基于霍尼韦尔WEBs的智能照明监控实训系统设计与实现

从建筑智能化专业楼宇自控系统工程技术课程的实训教学实际需要出发,以模拟实际智能建筑现场为背景,开发了智能照明监控实训系统。该系统使用霍尼韦尔WEBs为系统软件平台,应用霍尼韦尔Spyder DDC控制器PUB6438S作为系统的控制核心,利用人体红外感应、定时时间和光照度感应等方法进行实时控制。学生利用该实训教学系统可以更加深刻理解楼控系统软硬件开发的整个生命周期,对楼控项目设计和编程调试等应用技术进行系统学习和实训,从而提升职业院校学生的工程实践能力。