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EMfor_neural_networks
- In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial Co
Fast-K-means-clustering
- Fast mex K-means clustering algorithm with possibility of K-mean++ initialization (mex-interface modified from the original yael package https://gforge.inria.fr/projects/yael) - Accept single/double precision input - Support of BLAS/OpenMP
ksvdbox12
- 采用KSVD算法通过训练的方法来构造稀疏过完备字典，在使用时一定要确保已装有ompbox9。可用于语音，图像信号处理等的稀疏字典构造-KSVD algorithm using the method of training to construct the sparse over-complete dictionary, in use, make sure have been installed ompbox9. Can be used for the sparse dictionary cons
Matlab_zuixiaoerchengfa
- 最小费用最大流算法通用Matlab函数 基于Floyd最短路算法的Ford和Fulkerson迭加算法 基本思路：把各条弧上单位流量的费用看成某种长度,用Floyd求最短路的方法确定一条 自V1至Vn的最短路 再将这条最短路作为可扩充路,用求解最大流问题的方法将其上的流 量增至最大可能值 而这条最短路上的流量增加后,其上各条弧的单位流量的费用要重新 确定,如此多次迭代,最终得到最小费用最大流.- Minimum cost
Matlab_zuixiaofeiyong
- 最小费用最大流算法通用Matlab函数 基于Floyd最短路算法的Ford和Fulkerson迭加算法 基本思路：把各条弧上单位流量的费用看成某种长度,用Floyd求最短路的方法确定一条 自V1至Vn的最短路 再将这条最短路作为可扩充路,用求解最大流问题的方法将其上的流 量增至最大可能值 而这条最短路上的流量增加后,其上各条弧的单位流量的费用要重新 确定,如此多次迭代,最终得到最小费用最大流.-Minimum cost maximum fl
EMdemo
- EM算法在神经网络中的应用，可以用来进行视频数据分类。-In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Wil
ISOMAP.MATLAB
- ISOMAP算法的matlab源程序，可直接运行，对于matlab的初学者有很好的借鉴意义，强烈推荐-It is a process written for ISOMAP algorithm.I am sure you will enjoy it
akbarali8765234
- The Matlab project contains the source code and Matlab used for a very fast subpixel image registration . A very fast and accuracy subpixel image registration or alignment based on cross correlation and modified moment algorithm . The source cod
sherly2010
- This is a two stage fraud detection system which compares the incoming transaction against the transaction history to identify the anomaly using BOAT algorithm in the first stage. In second stage to reduce the false alarm rate suspected anomalies