基于独立分量分析的虹膜识别方法

虹膜识别技术作为一种生物识别手段，具有惟一性、稳定性和安全性等优点，从而成为当前模式识别和机器学习领域的一个研究热点．提出了一种新的虹膜识别方法，该方法利用独立分量分析(ICA)提取虹膜的纹理特征，并采用竞争学习机制进行识别．实验结果证明了该方法的有效性和对环境的适应性，在图像模糊、噪声干扰等不利条件下，仍然能够正确识别．     

面向类别不平衡数据的主动在线加权极限学习机算法

针对在样本类别分布不平衡场景下,现有的主动学习算法普遍失效及训练时间过长等问题,提出采用建模速度更快的极限学习机,即ELM(Extreme Learning Machine)作为主动学习的基分类器,并以加权ELM算法用于主动学习过程的平衡控制,进而在理论上推导了其在线学习的过程,大幅降低了主动学习的时间开销,并将最终的混合算法命名为AOW-ELM算法。通过12个基准的二类不平衡数据集验证了该算法的有效性与可行性。     

基于多Agent的并行Q-学习算法

提出了一种多Agent并行Q-学习算法．学习系统中存在多个Agent，它们的学习环境、学习任务及自身功能均相同，在每个学习周期内，各个Agent在各自独立的学习环境中进行学习，当一个学习周期结束后，对各个Agent的学习结果进行融合，融合后的结果被所有的Agent共享，并以此为基础进行下一个周期的学习．实验结果表明了该方法的可行性和有效性。     

Explorations of an Incremental,Bayesian Algorithm for Categorization

An incremental categorization algorithm is described which, at each step, assigns the next instance to the most probable category. Probabilities are estimated by a Bayesian inference scheme which assumes that instances are partitioned into categories and that within categories features are displayed independently and probabilistically. This algorithm can be shown to be an optimization of an ideal Bayesian algorithm in which predictive accuracy is traded for computational efficiency. The algorithm can deliver predictions about any dimension of a category and does not treat specially the prediction of category labels. The algorithm has successfully modeled much of the empirical literature on human categorization. This paper describes its application to a number of data sets from the machine learning literature. The algorithm performs reasonably well, having its only serious difficulty because the assumption of independent features is not always satisfied. Bayesian extensions to deal with nonindependent features are described and evaluated.     

贝叶斯网学习中一种有效的爬山算法

提出在学习贝叶斯网下的一种行之有效的爬山算法,HCBest算法.该算法在学习网络结构形成环时,选择删除能提高打分值最多的边,直到没有环为止.实验证明,HCBest既可以作为一种独立的贝叶斯网学习方法,又可以作为其它复杂元启发方法的局部搜索算法.HCBest学出的网络在打分质量和结构上都比较好.在算法的简洁性和稳定性方面,HCBest的表现也令人满意.     

交互协调强化学习下的城市交通信号配时决策

针对应用传统强化学习进行城市自适应交通信号配时决策时存在维数灾难和缺乏协调机制等问题,提出引入交互协调机制的强化学习算法。以车均延误为性能指标设计了针对城市交通信号配时决策的独立Q-强化学习算法。在此基础上,通过引入直接交互机制对独立强化学习算法进行了延伸,即相邻交叉口交通信号控制agent间直接交换配时动作和交互点值。通过仿真实验分析表明,引入交互协调机制的强化学习的控制效果明显优于独立强化学习算法,协调更有效,并且其学习算法具有较好的收敛性能,交互点值趋向稳定。     

一种新的多智能体Q学习算法

针对非确定马尔可夫环境下的多智能体系统,提出了一种新的多智能体Q学习算法.算法中通过对联合动作的统计来学习其它智能体的行为策略,并利用智能体策略向量的全概率分布保证了对联合最优动作的选择. 同时对算法的收敛性和学习性能进行了分析.该算法在多智能体系统RoboCup中的应用进一步表明了算法的有效性与泛化能力.     

A learning approach to the bandwidth multicolouring problem

In this article, a generalisation of the vertex colouring problem known as bandwidth multicolouring problem (BMCP), in which a set of colours is assigned to each vertex such that the difference between the colours, assigned to each vertex and its neighbours, is by no means less than a predefined threshold, is considered. It is shown that the proposed method can be applied to solve the bandwidth colouring problem (BCP) as well. BMCP is known to be NP-hard in graph theory, and so a large number of approximation solutions, as well as exact algorithms, have been proposed to solve it. In this article, two learning automata-based approximation algorithms are proposed for estimating a near-optimal solution to the BMCP. We show, for the first proposed algorithm, that by choosing a proper learning rate, the algorithm finds the optimal solution with a probability close enough to unity. Moreover, we compute the worst-case time complexity of the first algorithm for finding a 1/(1–?) optimal solution to the given problem. The main advantage of this method is that a trade-off between the running time of algorithm and the colour set size (colouring optimality) can be made, by a proper choice of the learning rate also. Finally, it is shown that the running time of the proposed algorithm is independent of the graph size, and so it is a scalable algorithm for large graphs. The second proposed algorithm is compared with some well-known colouring algorithms and the results show the efficiency of the proposed algorithm in terms of the colour set size and running time of algorithm.     

Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each example according to a current hypothesis. Then the learner updates the hypothesis, if necessary, based on the correct classification of the example. One natural measure of the quality of learning in this setting is the number of mistakes the learner makes. For suitable classes of functions, learning algorithms are available that make a bounded number of mistakes, with the bound independent of the number of examples seen by the learner. We present one such algorithm that learns disjunctive Boolean functions, along with variants for learning other classes of Boolean functions. The basic method can be expressed as a linear-threshold algorithm. A primary advantage of this algorithm is that the number of mistakes grows only logarithmically with the number of irrelevant attributes in the examples. At the same time, the algorithm is computationally efficient in both time and space.     

基于独立分量分析的笔迹识别

笔迹识别作为一种身份识别技术,具有自然,非入侵等优点,因此成为模式识别和机器学习领域的一个研究热点。本文提出了一种与文本无关的笔迹识别方法,该方法利用独立分量分析(Independent Component Analysis , ICA)来提取笔迹的纹理特征,并利用竞争学习方法确定笔迹的特征编码。实验结果证明利用该方法进行笔迹识别具有很好的效果。     

Reinforcement Learning Transfer Based on Subgoal Discovery and Subtask Similarity

This paper studies the problem of transfer learning in the context of reinforcement learning. We propose a novel transfer learning method that can speed up reinforcement learning with the aid of previously learnt tasks. Before performing extensive learning episodes, our method attempts to analyze the learning task via some exploration in the environment, and then attempts to reuse previous learning experience whenever it is possible and appropriate. In particular, our proposed method consists of four stages: 1) subgoal discovery, 2) option construction, 3) similarity searching, and 4) option reusing. Especially, in order to fulfill the task of identifying similar options, we propose a novel similarity measure between options, which is built upon the intuition that similar options have similar stateaction probabilities. We examine our algorithm using extensive experiments, comparing it with existing methods. The results show that our method outperforms conventional non-transfer reinforcement learning algorithms, as well as existing transfer learning methods, by a wide margin.      

李群深层结构学习算法研究

针对数据的复杂性和语义深层关系,提出一种李群深层结构学习算法。主要包括:基于流形的深层结构分析方法、基于参数的李群半监督学习算法和基于线性的李群半监督学习算法,以及这些算法相融合的李群深层结构学习算法。该算法对连续语义间的深层关系有着重要的作用。实验结果显示,深度越深,该算法的效果越好。     

基于蒙特卡罗学习的多机器人自组织协作

强化学习是提高机器人完成任务效率的有效方法,目前比较流行的学习方法一般采用累积折扣回报方法,但平均值回报在某些方面更适于多机器人协作。累积折扣回报方法在机器人动作层次上可以提高性能,但在多机器人任务层次上却不会得到很好的协作效果,而采用平均回报值的方法,就可以改变这种状态。本文把基于平均值回报的蒙特卡罗学习应用于多机器人合作中,得到很好的学习效果,实际机器人实验结果表明,采用平均值回报的方法优于累积折扣回报方法。     

基于参照的对词结构操作语义的归纳学习

心理语言学的研究和认知发展过程证明在语言获得的早期经历了一个自主的归纳学习过程,本文的出发点是语言发展的规律,并将词结构形式语义的获得过程和表示基础放在一个具有统一的语言理解和语言产生机制的语言信息加工模型中来考虑。本文讨论了一个基于实例的机器学习系统,为了获得词结构的形式语义,采用了操作语义的定义,并设计了一个基于参照的发现学习算法,其目的是使语义能伴随例句样本的丰富而精密化。     

非线性系统闭环P型迭代学习控制的收敛性

本文得到并证明了当被控系统的状态方程为一类非线性方程时，采用闭环Ｐ型学习律迭代学习控制的收敛的充分条件和必要条件，最后，我们给出了典型的仿真结果。     

一种结合演示数据和演化优化的强化学习方法

强化学习研究智能体如何从与环境的交互中学习最优的策略,以最大化长期奖赏。由于环境反馈的滞后性,强化学习问题面临巨大的决策空间,进行有效的搜索是获得成功学习的关键。以往的研究从多个角度对策略的搜索进行了探索,在搜索算法方面,研究结果表明基于演化优化的直接策略搜索方法能够获得优于传统方法的性能;在引入外部信息方面,通过加入用户提供的演示,可以有效帮助强化学习提高性能。然而,这两种有效方法的结合却鲜有研究。对用户演示与演化优化的结合进行研究,提出iNEAT+Q算法,尝试将演示数据通过预训练神经网络和引导演化优化的适应值函数的方式与演化强化学习方法结合。初步实验表明,iNEAT+Q较不使用演示数据的演化强化学习方法NEAT+Q有明显的性能改善。     

基于半监督学习的多示例多标记E-MIMLSVM+算法

多示例多标记是一种新的机器学习框架,在该框架下一个对象用多个示例来表示,同时与多个类别标记相关联。MIMLSVM+算法将多示例多标记问题转化为一系列独立的二类分类问题,但是在退化过程中标记之间的联系信息会丢失,而E-MIMLSVM+算法则通过引入多任务学习技术对MIMLSVM+算法进行了改进。为了充分利用未标记样本来提高分类准确率,使用半监督支持向量机TSVM对E-MIMLSVM+算法进行了改进。通过实验将该算法与其他多示例多标记算法进行了比较,实验结果显示,改进算法取得了良好的分类效果。     

A diversity maintaining population-based incremental learning algorithm

In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population. We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploration of the search space. The presented probabilistic algorithm is specifically for applications in the binary domain. The benchmark data used for the experiments are commonly used deceptive and attractor basin functions as well as 10 common travelling salesman problem instances. Our experimental results focus on the effect of parameters and problem size on the accuracy of the algorithm as well as on a comparison to traditional population-based incremental learning. We show that the new algorithm is able to effectively utilize the increased diversity of opposition which leads to significantly improved results over traditional population-based incremental learning.     

轴承故障的智能诊断方法

总结了轴承故障诊断的模糊规则,并把这些规则晨射到模糊神经网络,提高了诊断的智能性。提出了部分层学习算法,并推导出隶属度函数的参数学习算法,改善了诊断规则和学习性能,所研制的诊断系统在对轴承邦联伯诊断中取得了满意的结果。     

基于指数损失间隔的多标记特征选择算法

在多标记学习的任务中,多标记学习的每个样本可被多个标签标记,比单标记学习的应用空间更广关注度更高,多标记学习可以利用关联性提高算法的性能。在多标记学习中,传统特征选择算法已不再适用,一方面,传统的特征选择算法可被用于单标记的评估标准。多标记学习使得多个标记被同时优化;而且在多标记学习中关联信息存在于不同标记间。因此,可设计一种能够处理多标记问题的特征选择算法,使标记之间的关联信息能够被提取和利用。通过设计最优的目标损失函数,提出了基于指数损失间隔的多标记特征选择算法。该算法可以通过样本相似性的方法,将特征空间和标记空间的信息融合在一起,独立于特定的分类算法或转换策略。优于其他特征选择算法的分类性能。在现实世界的数据集上验证了所提算法的正确性以及较好的性能。