资源列表
dbms
- Greedy algorithm descr iption with some examples-Greedy algorithm descr iption with some examples.............
feinen-V3.6
- Target can be extracted in a picture you want, Convolution operation is intended to signal and image rendering, gmcalab fast generalized form component analysis.
SimplePerceptronNuronalNetwork
- This a example about programming a neural network-This is a example about programming a neural network
pom
- C Implementation of Probabilistic Occupancy Map, which can be used for object tracking with multiple views
tspGeneticAlgorithm
- 一个遗传算法求解TSP问题的具体实现，采用C++实现，可以采用网上提供的城市节点数据测试-A genetic algorithm for TSP on the specific implementation, using C++ implementation, available online the city can use the node data test
MATLAB_Cppgraphiclib
- 主要描述了vc与matlab联合仿真的应用，通过简单的实例，可以很快的学会操作。-Vc describes the co-simulation with matlab applications, through a simple example, can quickly learn to operate.
ANN-collections
- ann matlab programs neural network
cluster
- 基于kemans算法的聚类，初始点投放基于最远距离的方法-Clustering algorithm based on kemans, the initial point of delivery methods based on the most remote
yichuansuanfajiejueTSPwentiyanshi
- 旅行商问题，即TSP问题（Traveling Salesman Problem）是数学领域中著名问题之一-Traveling Salesman Problem, or TSP problem (Traveling Salesman Problem) is a well-known field of mathematics one of the issues
6
- SOM神经网络的数据分类 非常具体的实验报告 根据SOM神经网络相关知识，设计一个具有数据分类功能的自组织映射神经网络。要求该网络可以正确地对样本中包含的数据集进行分类。-Data SOM neural network classification very specific test report SOM neural network based on knowledge, to design a self-organizing map data classification ne
BPandmatlab
- 针对标准 算法存在的缺陷，本文给出了基于matlab语言的神经网络几种改进的算法- 阐述了各种算法的优化技术原理、优缺点，并就它们的训练速度和内存消耗情况作了比较-According to the defects of standard algorithm, this paper presents the neural network based on matlab language several improvement algorithm, expounds the optimization
Exercise2-Vectorization
- http://deeplearning.stanford.edu/wiki/index.php/Exercise:Vectorization。斯坦福深度学习的教程，练习2的代码-http://deeplearning.stanford.edu/wiki/index.php/Exercise:Vectorization. Stanford deep learning tutorials, exercises 2 code