搜索资源列表
BP-Classification
- 基于BP神经网络的模式识别方法，基于简单易懂的特点，同时有助于深刻理解神经网络的学习训练方法。-BP neural network pattern recognition method based on the characteristics of easy to understand, while helping a deep understanding of the neural network learning and training methods.
DeepLearnToolbox_CNN_lzbV2.0
- DeepLearnToolbox_CNN_lzbV2.0 深度学习,卷积神经网络,Matlab工具箱 参考文献: [1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006 [2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998 [3] https://github.com/rasmusberg
发给各中心通知
- CNN is one of the most successful deep learning methods due to its remarkable performance on ImageNet large-scale visual recognition competition. The success of CNN is due to its capacity to learn hierarchical representation to describe the image
Matlab-libsvm-3.20
- SVM(Support Vector Machine)指的是支持向量机，是常见的一种判别方法。在机器学习领域，是一个有监督的学习模型，通常用来进行模式识别、分类以及回归分析。 Vapnik等人在多年研究统计学习理论基础上对线性分类器提出了另一种设计最佳准则。其原理也从线性可分说起，然后扩展到线性不可分的情况。甚至扩展到使用非线性函数中去，这种分类器被称为支持向量机(Support Vector Machine，简称SVM)。支持向量机的提出有很深的理论背景。 支持向量机方法是在后来提出的
stanford-deep-learning-matlab-code
- Stanford 大学的深度学习源代码，可用于模式识别和预测，比较稳定。(Stanford University's deep learning source code can be used for pattern recognition and prediction, and is relatively stable.)
雷达信号分选源码
- 用MATLAB深度学习进行雷达辐射源信号分选识别(Radar emitter signal sorting and recognition with MATLAB deep learning)