An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.     

基于微信公众平台的外语微学习资源设计与应用研究

针对微信使用群体的普及,本文提出在构建外语学习资源信息管理系统的基础上,搭建一个用于学生校园学习的微信服务账号,从而通过该微信服务账号实现学生对学习资源的利用,更好的促进语言能力的提升。本文借助Web服务器、Java技术、数据库、XML解析技术等系统进行开发,并对其实现进行详细的阐述,从而实现信息化技术与教学方式的创新,具有很大的推广和应用价值。     

一种新型的在瑞利信道下盲检测器设计

针对采用卡尔曼滤波或线性预测方案进行信号检测时,需要已知信道模型参数或预先估计信道参数这一局限性问题,提出一种新型的瑞利信道下的盲检测器。盲检测器通过设计粒子学习算法,能够在未知的信道衰落模型参数和噪声模型参数条件下检测信号。为了提升检测器的性能,采用信号延迟判决技术。仿真结果表明,盲检测器性能优异,已接近在已知信道模型参数下检测器的性能。该盲检测器易于并行设计实现,具有很高的实用价值。     

基于MG-LTP与ELM的微表情识别

特征提取和表情分类是表情识别的关键技术。针对传统方法识别率低的缺点,首先,提出了一种基于平均灰度的局部三值模式(MG-LTP)新算法,用于提取表情特征;其次,使用极限学习机(ELM)作为分类器,用于特征分类;最后,将二者结合用于表情识别,并进一步应用于人脸微表情识别中。在JAFFE数据库及CASME人脸微表情数据库进行试验,与传统方法对比,取得了较好的效果。     

Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints

Recent literature has demonstrated the applicability of genetic programming to induction of decision trees for modelling toxicity endpoints. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node that will necessarily miss regions of the search space, the genetic programming based approach can overcome the problem. However, the method still requires the discretization of the often continuous-valued toxicity endpoints prior to the tree induction. A novel extension of this method, YAdapt, is introduced in this work which models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built.     

基于改进极根学习机的回转窑煅烧带温度预测方法

针对传统算法预测回转窑煅烧带温度存在精度低、速度慢的问题,提出了基于改进极限学习机(ELM)的回转窑煅烧带温度预测方法。对ELM输入权值矩阵定义了变换系数,采用黄金分割法在给定区间内搜寻变换系数的最佳值,改进了ELM网络参数的确定方式,弥补了随机确定输入权值并且不作调整的缺陷,在保证ELM训练速度的前提下提高预测精度、减小模型随机性。实验结果表明,改进的ELM预测精度高、训练速度快、模型性能优,可满足工况恶劣的回转窑的生产需要。     

基于MetropolisQ学习的蜂窝网络在线信道分配

针对现有的蜂窝网络的在线动态分配模型具有的信道需求量大、呼叫动态变化时阻塞率高和收敛速度慢的缺点,设计了一种基于MetropoisQ学习的蜂窝网络的在线信道分配方法。首先,在考虑同信道限制、邻居信道限制和同小区限制的基础上,设计了在线信道分配的数学模型,然后在Q-Learning算法基础上的设计了一种基于资格迹的Q(λ)算法实现信道的在线分配,为了进一步提高收敛速度,采用Metropois规则对算法中动作的选择方式进行改进,实现探索和利用的平衡。为了验证文中方法,采用Matlab工具上进行实验,仿真实验结果表明文中方法能实现蜂窝通信网络的在线信道分配,且与其它方法比较,具有较少的信道需求量、较低的阻塞率和收敛速度,较其它方法具有较大优越性。     

Cooperative Q‐learning techniques for distributed online power allocation in femtocell networks

In this paper, we address the problem of distributed interference management of femtocells that share the same frequency band with macrocells using distributed multi‐agent Q‐learning. We formulate and solve two problems representing two different Q‐learning algorithms, namely, femto‐based distributed and sub‐carrier‐based distributed power controls using Q‐learning (FBDPC‐Q and SBDPC‐Q). FBDPC‐Q is a multi‐agent algorithm that works on a global basis, for example, deals with the aggregate macrocell and femtocell capacities. Its complexity increases exponentially with the number of sub‐carriers in the system. Also, it does not take into consideration the sub‐carrier macrocell capacity as a constraint. To overcome these problems, SBDPC‐Q is proposed, which is a multi‐agent algorithm that works on a sub‐carrier basis, for example, sub‐carrier macrocell and femtocell capacities. Each of FBDPC‐Q and SBDPC‐Q works in three different learning paradigms: independent (IL), cooperative (CL), and weighted cooperative (WCL). IL is considered the simplest form for applying Q‐learning in multi‐agent scenarios, where all the femtocells learn independently. CL and WCL are the proposed schemes in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. We prove the convergence of the CL paradigm when used in the FBDPC‐Q algorithm. We show via simulations that the CL paradigm outperforms the IL paradigm in terms of the aggregate femtocell capacity, especially in networks with large number of femtocells and large number of power levels. In addition, we propose WCL to address the CL limitations. Finally, we evaluate the robustness and scalability of both FBDPC‐Q and SBDPC‐Q, against several typical dynamics of plausible wireless scenarios (fading, path loss, random activity of femtocells, etc.). We show that the CL paradigm is the most scalable to large number of femtocells and robust to the network dynamics compared with the IL and WCL paradigms. Copyright © 2014 John Wiley & Sons, Ltd.     

结合PBL课例的无机化学线下对分课堂教学设计与实践*

以原电池一节教学设计为例,探索了将PBL课例融入对分课堂教学模式。教学实践表明,新教学模式激发了学生的学习兴趣,提高了学生学习的积极性和主动性,提升了课程教学效果,达到了课程教学目标。对PBL课例与对分课堂有机融合的全新课堂教学模式的建设与发展进行了展望。     

基于深度学习理论的有机合成课程教学改革

有机合成既是有机化学学习中的一个独立构成部分,也是安排于有机化学课程之中的重要学习主题。在目前的有机合成课程教学中,教师的自觉性引导不足,学生容易陷入浅层学习状态,导致教学效果不佳。结合有机化学学科特点,从有机合成课程教学现状出发,围绕深度学习教学设计的四要素——深度学习目标、挑战性学习主题、深度学习活动、持续性评价,以典型的教学片段为例阐述深度学习理论在教学实践中的应用,为相关教学工作提供参考。