搜索资源列表
era
- 基于时域的模态参数识别算法，特征系统实现法。-Modal parameter identification algorithm based on time domain, the characteristics of the system implementation method.
SVM_regression_1001
- 一种可以择优选择参数的SVM拟合回归程序，通过for循环，可以调节参数，调出误差最小的参数设置。有时候，默认的参数也会有很不错的精度。-One can choose the best parameters of the SVM fitting regression procedures, through the for cycle, you can adjust the parameters, minimize the error of the parameter settings. Somet
multiverso-master
- Multiverso is a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces. With such easy-to-use APIs, m
distributed_word_embedding-master
- The Distributed Word Embedding tool is a parallelization of the Word2Vec algorithm on top of our DMTK parameter server. It provides an efficient scaling to industry size solution for word embedding. -The Distributed Word Embedding tool is a paralle
distributed_skipgram_mixture-master
- The Distributed Multisense Word Embedding(DMWE) tool is a parallelization of the Skip-Gram Mixture [1] algorithm on top of the DMTK parameter server. It provides an efficient scaling to industry size solution for multi sense word embedding. -The Di
fengqunsuanfaPID
- 针对工业控制中常用的PID控制器参数整定困难的问题,提出一种基于人工蜂群算法的参数整定方法。将PID控制器待整定参数看作蜜源,利用蜂群特有的角色转变机制搜索优质的参数组合 选取绝对误差矩积分性能指标作为参数寻优的目标函数。仿真实验结果表明,所采用的算法能够提高控制系统的动态性能,增强系统的快速性和稳定性,适用于PID控制器的自整定。 -For industrial control commonly used in PID controller tuning difficult problem
multi-dimensional-particle
- 一维及二维数据粒子算法寻优,包括1）二维粒子算法参数寻优-替代网格寻优；2）一维数据粒子算法寻优（极值）。- One dimensional and two dimensional data particle algorithm optimization, including 1) two dimensional particle algorithm parameter optimization- alternative grid optimization 2) one dimens
yalemain
- 人脸识别，验证参数错误率，选择错误率小的参数 （对yale人脸库进行人脸识别），代码完整-Face recognition, validate parameter error rates, choose the parameters of the error rate is small (library of Yale face for face recognition), code integrity
apcluster.m
- ap算法完成ap聚类操作 需要输入参数为数据集 偏向参数 输出结果为聚类数目(The AP algorithm completes the AP clustering operation, the input parameter is the data set bias parameter, and the output result is the number of clusters)
alexnet
- ALEXNET较为简单的一种实现，参数调整比较方便，数据容易替换(ALEXNET a simpler implementation, parameter adjustment more convenient, easy to replace the data)