WWW业务访问特性分布研究

WWW业务表现为一系列的访问序列。而Web Server和Proxy Server的日志很好地记录了这种访问序列的过程及特性。WWW业务的特性研究是Web Server、Web中间件研究和人工合成Web负载的基础。分析了一个Web Server和两个Proxy Server的日志,重点研究了Web页面请求的概率分布、Web静态文档大小的概率分布（含传输文档）、Web静态文档的访问距离的概率分布,并将分析结果同相关文献的结果进行了对比,同时通过试验证实了在使用Size作为Web缓存替换依据时,还应该考虑Web文档的访问频率。     

基于知觉加工模式的发展式分词算法

分词是自然语言理解的一个重要过程,由于语言问题又是最重要的心理学问题之一,所以从认知心理学和发展心理学的观点出发,将分词问题看成是知觉问题在语言信息处理过程中的一部分,把知觉表达和知觉的双向加工过程引入对分词问题的分析,并同时考虑言语发展的心理过程,把对言语发展规律的归纳作为构造学习算法的基础和模板,以进化的方式来逐步改进自然语言理解系统的性能,从而避免知识获取瓶颈在自然语言理解问题中的出现。     

基于可靠多播的交互式远程教学环境的研究和框架设计

在广域网环境下,实现基于IP组播网络的交互式远程教学环境的可行性进行了简要的分析,在此基础上提出了一种基于可靠多播技术,及易于扩避孕药的系统框架设计方案。     

知识获取技术及其在网络智能故障诊断中的应用

基于遗传算法提出了一种新型的机器学习方法,它利用遗传算法的全局寻优手段,为解决知识获取这一瓶颈问题另辟捷径。论述了该方法的原理及其实现过程,并且给出了网络故障诊断应用实例,证明了它的有效性。     

基于增强型算法并能自动生成规则的模糊神经网络控制器

给出了一种基于增强型算法并能自动生成控制规则的模糊神经网络控制器RBFNNC（reinforcements based fuzzy neural network comtroller）。该控制器能根据被控对象的状态通过增强型学习自动生成模糊控制规则,RBFNNC用于倒立摆小车平衡系统控制的仿真实验表明了该系统的结构及增强型学习算法是有效和成功的。     

Investigation on AQ11，ID3 and the Principle of Discernibility Matrix

The principle of discernibility matrix serves as a tool to discuss and analyze two algorithms of traditional inductive machine learning,AQ11 and ID3.The results are:(1) AQ11 and its family can be completely specified by the principle of discernibility matrix;(2) ID3 and be partly,but not naturally,specified by the principle of discernibility matrix;and (3) The principle of discernibility matrix is employed to analyze Cendrowska sample set,and it shows the weaknesses of knowledge representation style of decision tree in theory.     

光三角式传感器测距系统

主要介绍了光三角式距离传感器测距系统的结构组成和原理 ,分析了参数之间的转换关系 ,论述了以单片机为核心构成检测电路的原理和性能 ,以及单片机的软件功能。最后给出检测系统的主要技术指标。     

可用于人脸识别的反馈型二元神经网络

提出和分析了一种新型的反馈型随机神经网络,并将其用于解决复杂的人脸识别问题.该模型采用随机型加权联接,神经元为简单的非线性处理单元.理论分析揭示该网络模型存在唯一的收敛平稳概率分布,当网络中神经元个数较多时,平稳概率分布逼近于Boltzmann-Gibbs分布,网络模型与马尔可夫随机场之间存在密切关系.在设计了一种新型模拟退火和渐进式Boltzmann学习算法后,系统被成功地应用于难度较大的静态和动态人像识别,实验结果证实了系统的可行性和高效率.     

Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes

This article proposes several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes with finite state-space under the average cost criterion. Two of the algorithms are for the compact (non-discrete) action setting while the rest are for finite-action spaces. On the slower timescale, all the algorithms perform a gradient search over corresponding policy spaces using two different Simultaneous Perturbation Stochastic Approximation (SPSA) gradient estimates. On the faster timescale, the differential cost function corresponding to a given stationary policy is updated and an additional averaging is performed for enhanced performance. A proof of convergence to a locally optimal policy is presented. Next, we discuss a memory efficient implementation that uses a feature-based representation of the state-space and performs TD(0) learning along the faster timescale. The TD(0) algorithm does not follow an on-line sampling of states but is observed to do well on our setting. Numerical experiments on a problem of rate based flow control are presented using the proposed algorithms. We consider here the model of a single bottleneck node in the continuous time queueing framework. We show performance comparisons of our algorithms with the two-timescale actor-critic algorithms of Konda and Borkar (1999) and Bhatnagar and Kumar (2004). Our algorithms exhibit more than an order of magnitude better performance over those of Konda and Borkar (1999).

Privacy-preserving boosting

We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms. The algorithms inherit the excellent generalization performance of AdaBoost. Experiments indicate that the algorithms are better than AdaBoost executed separately by the participants, and that, independently of the number of participants, they perform close to AdaBoost executed using the entire data set. Responsible Editor: Charu Aggarwal.