Studies in Learning Automata and Pattern Recognition

学习自动机和模式识别研究

• 批准号: RGPIN-2015-05205
• 负责人: Oommen, John
• 金额: $ 2.11万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=661221
• 关键词: Studies; Learning; Automata; Pattern; Recognition

***I propose to undertake various projects that concern the fields of Learning Automata (LA), Anti-Bayesian Pattern Recognition (PR) and Neural Networks (NNs).******LA have been used to model biological systems. They have attracted considerable interest because they can learn the optimal action when operating in stochastic environments. We have pioneered the design of LA that operate in a discretized probability space, and the design of, probably, the fastest LA. We have also used LA to solve numerous real-life hard problems such as optimal web polling, routing for MPLS traffic etc.******Anti-Bayesian PR: In the field of PR, although the moments of the densities of the classes have been used in classification, the properties of the Quantiles have been unused. Recently, we proposed a novel pioneering paradigm named CMQS, Classification by the Moments of Quantile Statistics, which specifically uses these. CMQS is essentially "anti-Bayesian" because it operates in a counter-intuitive manner - it compares the testing sample to a few samples distant from the mean. We have proposed the foundational theory of CMQS for many uni-dimensional and multi-dimensional distributions.******Proposed Work 1. Bayesian Pursuit Algorithms. The fastest family of LA, the Pursuit Algorithms (PAs), utilize estimates of the reward probabilities. These estimates have been of the Maximum Likelihood nature. We propose to investigate families of PAs that involves Bayesian estimates. The work here will primarily be analytic and foundational.******Proposed Work 2. "Elevator"-based LA Solutions. The entire field of LA deals with the model in which the actions that to be learnt are "unordered", i.e., the LA does not have any reason to believe that a1 is better than a2, and so on. However, if the actions can be ordered, they can be considered to be as levels in a building, whereby the learning problem is one of choosing where an elevator must station itself so as to minimize a user-defined criterion. Our research will involve modeling the learning paradigm, and analyzing the unexplored field of generalized "Elevator"-like LA. ******Proposed Work 3. Stochastic Point Location (SPL). Earlier, we had studied the use of LA in the so-called SPL problem and provided discretized, recursive and hierarchical solutions. We propose to design new recursive d-ary search solutions, and to use them in stochastic optimization. ******Proposed Work 4. Applications. We propose to use LA to solve the Goore Game in which the players can also be traitors, to solve the COMMONS game, a generalization of the Prisoner's Dilemma, and in cognitive radios.******Proposed Work 5. PR: We propose to work to develop the theory of Anti-Bayesian PR for non-parametric applications, kernel-based, and unsupervised schemes. We also will work on Chaotic PR.******Proposed Work 6. NN: We propose to develop NNs to enhance the Growing Self-Organizing Map (GSOM) which is a NN clustering algorithm based on Kohonen's Self-Organizing Map.**

* 我建议开展有关学习自动机（LA），反贝叶斯模式识别（PR）和神经网络（NN）领域的各种项目。LA已被用于模拟生物系统。它们吸引了相当大的兴趣，因为它们可以学习最佳的行动时，在随机环境中运行。我们开创了在离散概率空间中操作的LA的设计，以及可能是最快的LA的设计。我们还使用LA解决了许多现实生活中的难题，例如最佳Web轮询，MPLS流量路由等。反贝叶斯PR：在PR领域中，虽然类的密度的矩被用于分类，但分位数的性质一直未被使用。最近，我们提出了一种新的开创性范式，名为CMQS，分位数统计矩分类，它专门使用这些。CMQS本质上是“反贝叶斯”的，因为它以一种反直觉的方式运行-它将测试样本与远离平均值的几个样本进行比较。我们已经为许多一维和多维分布提出了CMQS的基础理论。建议工作1。贝叶斯追踪算法最快的LA家族，追踪算法（PA），利用奖励概率的估计。这些估计是最大可能性性质的。我们建议调查家庭的PA，涉及贝叶斯估计。这里的工作将主要是分析性和基础性的。建议工作2。电梯解决方案LA的整个领域涉及其中要学习的动作是“无序的”的模型，即，然而，如果动作可以被排序，则它们可以被认为是建筑物中的层，由此学习问题是选择电梯必须自身停靠在何处以便最小化用户定义的标准的问题。我们的研究将涉及建模的学习范式，并分析未开发的领域广义的“电梯”一样的LA。**随机点定位（SPL）。早些时候，我们研究了使用LA在所谓的SPL问题，并提供离散化，递归和分层的解决方案。我们建议设计新的递归d元搜索解决方案，并使用它们在随机优化。* *应用.我们建议使用LA来解决参与者也可以成为叛徒的古尔博弈，解决共同博弈，囚徒困境的推广，以及认知无线电。建议工作5。PR：我们建议为非参数应用、基于内核和无监督方案开发反贝叶斯PR理论。我们也将致力于混沌公关。*建议工作6。NN：我们建议开发NN来增强生长自组织映射（GSOM），这是一种基于Kohonen自组织映射的NN聚类算法。