资源列表
2016flower-pollination-algorithm
- 最新的智能算法，国内最具有代表性的关于花授粉算法的改进论文。-The latest intelligent algorithms, the most representative of the domestic research on the improvement of flower pollination algorithm.
optimization-modelroad-network
- 双层规划，禁忌算法，道路交通规划，多目标优化-Based on the optimization model of regional road network double tabu genetic algorithm Pan Genan
CF-with-RBM
- RBM的CF，单模型效果和SVD相似，只是error在不同的地方，所以结合起来可以提升效果，总觉得RBM不够intuitive，这次实现也遇到很多困难-RBM of CF, a single model and SVD similar effect, but error in different places, so you can combine to enhance the effect, always feel RBM enough intuitive, this realization
nn1
- 详细的BP神经网络的实现。通过此代码可以加强对算法本身的理解。-Implementation details of BP neural network.
ImmuneGeneric
- 基于疫苗的免疫遗传算法，将人工免疫算法与一串算法相结合，有比较好的效果-Vaccines based on immune genetic algorithm, artificial immune algorithm and a bunch of algorithms combined with relatively good results
Neural-Network
- 神经网络去噪+matlab程序+神经网络去除随机脉冲干扰-Neural network neural network denoising+matlab program+ removing random pulse interference
python_pnn
- Probabilistic Neural Network for binary classification in python. Also using K-Folding technique in python.
Sparse-Autoencoder
- 神经网络稀疏自编码器的实现；从给定的很多张自然图片中截取出大小为8*8的小patches图片共10000张，现在需要用sparse autoencoder的方法训练出一个隐含层网络所学习到的特征。-Sparse neural networks since implementation of the encoder interception of a size of 8* 8 picture small patches given a lot of sheets natural picture
Softmax_exercise
- Softmax用于多分类问题，本例是将MNIST手写数字数据库中的数据0-9十个数字进行分类，其中训练样本有6万个，测试样本有1万个数字是0~9-Softmax for multi classification problems, the present case is the handwritten data MNIST digital 0-9, classification, training samples which have 60,000, there are 10,000 test
ExerciseSelf-Taught-Learning
- Soft-taught leaning是用的无监督学习来学习到特征提取的参数，然后用有监督学习来训练分类器.-Soft-taught leaning unsupervised learning is to learn the parameters of feature extraction, followed by supervised learning to train the classifier.
trees
- 机器学习中的c4.5树的源代码，经测试，可以使用-The source code tree c4.5 in machine learning, through the test, can be used
email
- 机器学习算法的数据集，包含训练集和测试集。主要用于邮件分类-Machine learning algorithms of data sets, including training set and testing set.Mainly used for E-mail classification