资源列表
closed-form-low-rank-representation
- 可实现闭式解低秩子空间聚类，该程序特点：收敛速度较快，但是有多个参数需要调整。参考文献：Rene Vidal, Paolo Favaro. Low rank subspace clustering (LRSC) [J]. Pattern Recognition Letters, 2014, 43: 47-61.-This program can realize closed-form low rank subspace clustering. The characteristic of the
Latent-LRR
- 由文章作者提供的隐式低秩子空间聚类算法。参考文献：Guangcan Liu, Shuicheng Yan. Latent low rank representation [J]. Springer International Publishing, 2014:23-38. -The program is provided by the authors to realize latent low rank subspace clustering. Reference: Guangcan Liu,
NN-and-DL.pdf
- 神经网络和深度学习基础讲义，可作为基本概念的理解与学习。-Neural Networks and Deep Learning
LinWPSO
- 线性递减权重法的粒子群算法，针对基本粒子群算法容易早熟以及算法后期易在全局最优解附近产生振荡现象，可以采用线性变化的权重。-A linear weighting method of particle swarm algorithm, in view of the basic particle swarm optimization (pso) algorithm is easy to premature algorithm and easy to generate oscillation phen
PSO
- 基本粒子群优化算法，可快速求解无约束优化问题。-Basic particle swarm optimization algorithm, it can quickly solve unconstrained optimization problems.
SAPSO
- 为了平衡粒子群算法的全局搜索能力和局部改良能力，还可采用非线性的动态惯性权重公式。-In order to balance the global search ability of particle swarm optimization (pso) algorithm and local improvement ability, also can use nonlinear dynamic inertia weight formula.
LnCPSO
- 针对粒子群算法在实际的应用中，也有一些其他的取值方法，常见的有同步变化和异步变化的学习因子。-Aimed at particle swarm optimization (pso) algorithm in practical application, there are also some other accessor methods, common changes have synchronous and asynchronous learning factors.
LM实例
- 用L-M法求多维函数的极值，给出了一种求STLS解的算法及其子空间解释与拓扑解释,利用矩阵分解揭示了LM算法与STLS的密切关系
1filtr_Kalmana
- filtr kalman matlab kalman
cloud_matlab
- 用MATLAB 实现的综合云模型的实现 Realization of integrated cloud model realized by MATLAB - Realization of integrated cloud model realized by MATLAB
LVQ_BP
- BP神经网络和LVQ，实例，包含完整数据和程序-BP neural network and LVQ example, contains the complete data and procedures
MUSTer_code_v1.1
- 追踪目标，根据cvpr论文而写，十分有效，游泳。为matlab代码。-object tracking,get idea a cvpr paper.it is very useful and powerfull