On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carl
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to per...
artificial-neural-networks-based-技术资料
📦 资源总数：1940
💻 源代码：72313
🔌 电路图：2
基于人工神经网络的技术，是当今电子工程领域中最具创新力的研究方向之一，广泛应用于模式识别、自动控制、预测分析等多个前沿科技领域。通过模仿人脑处理信息的方式，人工神经网络能够解决复杂非线性问题，为机器学习和人工智能的发展提供了强大动力。本页面汇集了1940份精选资源，包括但不限于算法实现、案例研究及最新科研成果，旨在帮助工程师深入理解并掌握这一关键技术，加速项目开发进程。无论是初学者还是资深专...
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查看全部1940个资源 »模式识别学习综述.该论文的英文参考文献为303篇.很有可读价值.Abstract— Classical and recent results in statistical pattern recog
模式识别学习综述.该论文的英文参考文献为303篇.很有可读价值.Abstract— Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-c...
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve r
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, ...
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps t
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We t...
本人编写的incremental 随机神经元网络算法
本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. ...
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