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2007-Transfer learning via inter-task mappings for temporal difference learning 迁移学习中的另外一篇重要文献
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运动目标检测帧差法的源代码 opencv代码 自己调试运行通过，感觉效果还不错，适合本科毕设-Frame difference method for moving object detection source code opencv code debugging and running through, feeling pretty good, suitable for undergraduate Complete set
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使用java编写的GUI的黑白棋游戏，搜索算法采用经典的博弈树，并在此基础上做了大量优化，我的评估函数采用了Simon M. Lucas 和 Thomas P. Runarsson 在其合作发表的 Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation 中通过对比即时差分学习（TDL，Temporal Difference
Learning）和协同进化（CEL，Co-
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提出了一种用于运动阴影检测的Boosting判别模型.这种方法先利用Boosting在不同的特征空间来区分前景和阴影,然后在判别随机场(DRFs)中结合前景和阴影的时空一致性,实现对前景和阴影的分割.首先,差分前图像与背景图像得到颜色不变子空间和纹理不变子空间 然后在这两个子空间上应用Boosting来区分前景和阴影 最后利用前景和阴影的时空
一致性,在判别随机场中通过图分割的方法准确地分割前景和阴影
-A new method for the detection of movement
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使用java編寫的GUI的黑白棋遊戲-GUI using java prepared Riversi games, search algorithms using classical game tree, and on this basis have done a lot of optimization, the evaluation function I used Simon M. Lucas and Thomas P. Runarsson published in its Temporal
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以镜头光圈分类 镜头有手动光圈（manual iris）和自动光圈（auto iris）之分，配合摄象机使用，手动光圈镜头适合于亮度不变的应用场合，自动光圈镜头因亮度变更时其光圈亦作自动调整，故适用亮度变化的场合。-This paper describes an end-to-end method for extracting
moving targets from a real-time video stream, classifying
them into predefined ca
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背景差分法 Opencv 运动目标识别 瞬时差分法-Background subtraction method the Opencv movement target identification temporal difference method
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Finite difference methods:
Spatial discretization (3)
Temporal discretization (2)
Convergence, Consistency, Stability (2)
Solution of PDEs require spatial and temporal discretization
Typically, the spatially-discretized domain is call
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Temporal difference implementation
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利用基于像元的差值法对两个时相的遥感影像进行变化检测-Rmote sensing images change detection based on pixel-difference between two temporal images
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时序差分学习算法TD(lamda)求解N*N方格走迷宫问题，求解每个方格的V值-Temporal difference learning algorithm for N*N maze problem
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用openCV和vc实现的一个时间差分法，已经通过调试，可以直接运行。-Use openCV and vc realize a time difference method, has been debugging through, can be directly run.
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时空中心差分模拟二维SH波动，透射边界条件，无限均匀空间中的点源扩散问题-2D SH wave simulation using central difference method in both spatial and temporal domain, with transmitting boundary condition, point source
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时序差分学习是强化学习的一种重要算法，该代码提供了时序差分学习做路径规划的一个仿真。-Temporal difference learning is an important algorithm for reinforcement learning, which provides a simulation of sequential differential learning for path planning.
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时间方向利用龙格库塔方法，空间方向利用中心差分方法求解随机非线性薛定谔方程-Stochastic Schrodinger equation with finite difference methods in temporal and spatial direction
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讲解强化学习中的Q学习和sarsa学习，并通过具体实例讲解怎样运用这些学习方法(Explain Q learning and sarsa learning in intensive learning, and explain how to use these learning methods through concrete examples)
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