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kalmantargettraching

于 2010-08-23 发布文件大小:119KB

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下载积分: 1 下载次数: 28

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代码说明：

目标跟踪卡尔曼滤波平滑大全有外文介绍(kalman_filter kalman_forward_backward kalman_smoother)

文件列表：

卡尔曼滤波跟踪
..............\Kalman
..............\......\AR_to_SS.m,1107,2002-05-29
..............\......\convert_to_lagged_form.m,425,2002-05-29
..............\......\ensure_AR.m,354,2002-05-29
..............\......\eval_AR_perf.m,1045,2002-05-29
..............\......\kalman_filter.m,2899,2002-05-29
..............\......\kalman_forward_backward.m,2392,2002-11-01
..............\......\kalman_smoother.m,1584,2002-05-29
..............\......\kalman_update.m,1840,2002-05-29
..............\......\learning_demo.m,1022,2002-10-23
..............\......\learn_AR.m,819,2002-05-29
..............\......\learn_AR_diagonal.m,687,2002-05-29
..............\......\learn_kalman.m,5498,2002-05-29
..............\......\README.txt,485,2004-06-07
..............\......\README.txt~,535,2003-01-18
..............\......\sample_lds.m,1797,2003-01-24
..............\......\smooth_update.m,1199,2002-05-29
..............\......\SS_to_AR.m,579,2002-05-29
..............\......\tracking_demo.m,1960,2003-01-18
..............\kalman_intro.pdf,144971,2004-09-07

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1807.01622
深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

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现代控制理论经典书籍，不得不看呀，最近发现还是以前的书写的好(Modern control theory classic book, had behold, recently discovered or writing a good previous)

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