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针对Tri-training算法利用无标记样例时会引入噪声且限制无标记样例的利用率而导致分类性能下降的缺点,提出了AR-Tri-training(Tri-training with assistant and rich strategy)算法.提出辅助学习策略,结合富信息策略设计辅助学习器,并将辅助学习器应用在Tri-training训练以及说话声识别中.实验结果表明,辅助学习器在Tri-training训练的基础上不仅降低每次迭代可能产生的误标记样例数,而且能够充分地利用无标记样例以及在验证集上的错分样例信息.从实验结果可以得出,该算法能够弥补Tri-training算法的缺点,进一步提高测试率. 相似文献
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选取最大可能预测错误样例的主动学习算法 总被引:5,自引:1,他引:4
通过选取并提交专家标注最有信息量的样例,主动学习算法中可以有效地减轻标注大量未标注样例的负担.采样是主动学习算法中一个影响性能的关键因素.当前主流的采样算法往往考虑选取的样例尽可能平分版本空间.但这一方法假定版本空间中的每一假设都具有相同的概率成为目标函数,而这在真实世界问题中不可能满足.分析了平分版本策略的局限性.进而提出一种旨在尽可能最大限度减小版本空间的启发式采样算法MPWPS(the most possibly wrong-predicted sampling),该算法每次采样时选取当前分类器最有可能预测错误的样例,从而淘汰版本空间中多于半数的假设.这种方法使分类器在达到相同的分类正确率时,采样次数比当前主流的针对平分版本空间的主动学习算法采样次数更少.实验表明,在大多数数据集上,当达到相同的目标正确率时,MPWPS方法能够比传统的采样算法采样次数更少. 相似文献
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由于随机块模型能够有效处理不具有先验知识的网络,对其研究成为了机器学习、网络数据挖掘和社会网络分析等领域的研究热点.如何设计出具有模型选择能力的快速随机块模型学习算法,是目前随机块模型研究面临的一个主要挑战.提出一种精细随机块模型及其快速学习算法.该学习方法基于提出的模型与最小消息长度推导出一个新成本函数,利用期望最大化参数估计方法,实现了边评价模型边估计参数的并行学习策略,以此方式显著降低随机块模型学习的时间复杂性.分别采用人工网络与真实网络,从学习时间和学习精度两方面对提出的学习算法进行了验证,并与现有的代表性随机块模型学习方法进行了对比.实验结果表明:提出的算法能够在保持学习精度的情况下显著降低时间复杂性,在学习精度和时间之间取得很好的折衷;在无任何先验知识的情况下,可处理的网络规模从几百节点提高至几万节点.另外,通过网络链接预测的实验,其结果也表明了提出的模型及学习算法相比现有随机块模型和学习方法具有更好的泛化能力. 相似文献
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领域本体是对领域概念及其关系的一种高效合理的展现形式.在构建领域本体过程中,常常遇到的问题就是尽管本体概念完备但概念间关系复杂多样导致人工标记关系代价过高.使用无监督学习的关系抽取算法对包含丰富的领域概念的web信息进行抽取解决了这一问题.然而,传统的无监督学习的算法没有考虑到"单样例多概念对"的问题,导致最终抽取的概念关系不完整.本文利用交通领域的Web信息构建本体,将样例概念关系对权重引入传统的无监督学习方法Kmeans中,解决了此项问题并通过实验证明该算法取得了良好的效果. 相似文献
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针对建模不精确的机器人,提出了一种基于神经网络补偿的机器人轨迹跟踪稳定自适应控制方法,文中通过设计神经网络补偿器和自适应鲁棒控制项,有效地补偿了模型的不确定性部分和网络逼近误差.由于算法包含有补偿神经网络逼近误差的鲁棒控制项,实际应用中对神经网络规模的要求可以降低;而且神经网络连接权是在线调整的,不需要离线学习过程.理论表明算法能够保证跟踪误差及神经网络连接权估计最终一致有界,仿真结果也验证了算法的有效性. 相似文献
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基于采样策略的主动学习算法研究进展 总被引:2,自引:0,他引:2
主动学习算法通过选择信息含量大的未标记样例交由专家进行标记,多次循环使分类器的正确率逐步提高,进而在标记总代价最小的情况下获得分类器的强泛化能力,这一技术引起了国内外研究人员的关注.侧重从采样策略的角度,详细介绍了主动学习中学习引擎和采样引擎的工作过程,总结了主动学习算法的理论研究成果,详细评述了主动学习的研究现状和发展动态.首先,针对采样策略选择样例的不同方式将主动学习算法划分为不同类型,进而,对基于不同采样策略的主动学习算法进行了深入地分析和比较,讨论了各种算法适用的应用领域及其优缺点.最后指出了存在的开放性问题和进一步的研究方向. 相似文献
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Two learning methods for acquiring position evaluation for small Go boards are studied and compared. In each case the function to be learned is a position-weighted piece counter and only the learning method differs. The methods studied are temporal difference learning (TDL) using the self-play gradient-descent method and coevolutionary learning, using an evolution strategy. The two approaches are compared with the hope of gaining a greater insight into the problem of searching for "optimal" zero-sum game strategies. Using tuned standard setups for each algorithm, it was found that the temporal-difference method learned faster, and in most cases also achieved a higher level of play than coevolution, providing that the gradient descent step size was chosen suitably. The performance of the coevolution method was found to be sensitive to the design of the evolutionary algorithm in several respects. Given the right configuration, however, coevolution achieved a higher level of play than TDL. Self-play results in optimal play against a copy of itself. A self-play player will prefer moves from which it is unlikely to lose even when it occasionally makes random exploratory moves. An evolutionary player forced to perform exploratory moves in the same way can achieve superior strategies to those acquired through self-play alone. The reason for this is that the evolutionary player is exposed to more varied game-play, because it plays against a diverse population of players. 相似文献
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This article presents an A* search algorithm to be applied to path planning in a Chinese chess game, and uses multiple mobile
robots to present the scenario. The mobile robots have a cylindrical shape, and their diameter, height, and weight are 8 cm,
15 cm, and 1.5 kg, respectively. The controller of the mobile robots is a MCS-51 chip. We play the Chinese chess game using
multiple mobile robots according to the evaluation algorithm of the game, and calculate the displacement by the encoder of
a DC servomotor. The A* search algorithm can solve the shortest-path problem for the mobile robots from the starting point
to the target point on the chess board. The simulated results found the shortest path for the mobile robots (chess pieces)
moving to target points from their starting points in a collision-free environment. Finally, we implemented the experimental
results on a Chinese chess board using mobile robots. Users can play the Chinese chess game using the supervising computer
via a wireless RF interface. The scenario of the feedback of the Chinese chess game to the user interface uses an image system. 相似文献
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In this paper, we introduce a chess program able to adapt its game strategy to its opponent, as well as to adapt the evaluation function that guides the search process according to its playing experience. The adaptive and learning abilities have been implemented through Bayesian networks. We show how the program learns through an experiment consisting on a series of games that point out that the results improve after the learning stage. 相似文献
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Eduardo Vázquez-Fernández Carlos A. Coello Coello Feliú D. Sagols Troncoso 《Applied Soft Computing》2013,13(7):3234-3247
Here, we propose an evolutionary algorithm (i.e., evolutionary programming) for tuning the weights of a chess engine. Most of the previous work in this area has normally adopted co-evolution (i.e., tournaments among virtual players) to decide which players will pass to the following generation, depending on the outcome of each game. In contrast, our proposed method uses evolution to decide which virtual players will pass to the next generation based on the number of positions solved from a number of chess grandmaster games. Using a search depth of 1-ply, our method can solve 40.78% of the positions evaluated from chess grandmaster games (this value is higher than the one reported in the previous related work). Additionally, our method is capable of solving 53.08% of the positions using a historical mechanism that keeps a record of the “good” virtual players found during the evolutionary process. Our proposal has also been able to increase the competition level of our search engine, when playing against the program Chessmaster (grandmaster edition). Our chess engine reached a rating of 2404 points for the best virtual player with supervised learning, and a rating of 2442 points for the best virtual player with unsupervised learning. Finally, it is also worth mentioning that our results indicate that the piece material values obtained by our approach are similar to the values known from chess theory. 相似文献
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LEARNING OF RESOURCE ALLOCATION STRATEGIES FOR GAME PLAYING 总被引:1,自引:0,他引:1
Human chess players exhibit a large variation in the amount of time they allocate for each move. Yet, the problem of devising resource allocation strategies for game playing has not received enough attention. In this paper we present a framework for studying resource allocation strategies. We define allocation strategy and identify three major types of strategies: static, semi-dynamic, and dynamic. We then describe a method for learning semi-dynamic strategies from self-generated examples. We present an algorithm for assigning classes to the examples based on the utility of investing extra resources. The method was implemented in the domain of checkers, and experimental results show that it is able to learn strategies that improve game-playing performance. 相似文献
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张利群 《计算机工程与应用》2016,52(7):62-66
针对苏拉卡尔塔棋“机-机”博弈需要人工参与的弊端,提出了构建苏拉卡尔塔棋计算机网络博弈平台的必要性,通过博弈平台实现自动对弈,而构建计算机博弈平台的核心技术之一就是吃子算法的实现。介绍了苏拉卡尔塔棋的三个要素,给出了一种用于计算机博弈平台的苏拉卡尔塔棋的存储结构。使用这种结构,给出了棋局的表示方法,建立了吃子循环队列,进而完成了适合于计算机博弈平台的吃子算法,实现了棋规。实验结果表明,这种存储结构高效可靠,吃子算法运行正确。该吃子算法可以应用于苏拉卡尔塔棋博弈平台的构建,并且这种存储结构和吃子算法对设计完成其他棋类的计算机博弈平台具有一定的参考价值。 相似文献
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“久”棋是藏族人民的传统棋类游戏,游戏过程分为布局阶段和战斗阶段,布局的质量对弈棋结果影响很大。与围棋博弈智能软件战胜人类高手的情况比较,“久”棋博弈研究几乎空白。为了拓宽机器博弈研究的游戏范围,开发具有较高棋力的“久”棋软件,作者开展了基于棋型的“久”棋计算机博弈研究。通过实地考察,在四川阿坝地区采集了约300局有效的“久”棋对弈数据,提取了常见棋型,分别为棋型命名为三角、三子、二子、对角、四子等。在布局阶段,采用模式匹配算法提高棋型的匹配速度。在布局和战斗阶段,基于棋型,设计了具有优先级别的防守、攻击、连子策略。采用C语言开发了“久”棋博弈软件,该软件具有人人对弈、人机对弈、自动录制棋谱等功能。该软件在2016年四川省阿坝县第七届“体彩杯”藏棋比赛中成功开展了人机对弈,但是棋力有待提高。结果表明,基于棋型的攻防策略能够有效地应用于“久”棋计算机博弈。 相似文献