licheng

principal component analysis (PCA ) is a well known approach for dimensionality reduction of the feature space. It has been successfully applied in face recognition. The main idea is to decompose face images into a small set of feature images called eigenfaces, which can be considered as points in a linear subspace called “face space” or “eigenspace”

Investigate about wind power and solar power

说明：  基于Matlab的心肌细胞动作电位模型程序，包含程序说明和部分结果。(Matlab based on the cardiac myocyte action potential model program, including program descriptions and partial results.)

说明：  一个MATLAB的支持向量机程序，并包含几个使用实例(A MATLAB support vector machine procedures, and contains several use cases)

说明：  本程序利用球不变随机过程发模拟产生服从K分布的海杂波序列，可用于实验室研究海杂波的输入序列。(This procedure made use of spherically invariant random process simulation produces the distribution of sea clutter obey the K series can be used for laboratory studies of sea clutter in the input sequence.)

隐马尔可夫链模型和例程（包括前向、GwfTugA后向算法、Viterbi解码以及为了减少概率数值计算误差编写的对数OiJxViY运算程序）(Hidden Markov chain model and routines (including forward, backward GwfTugA algorithm, Viterbi decoding, and in order to reduce the probability of numerical computation error of the preparation of the logarithm of the OiJxViY computing program))

产生BCH码多项式，其中码字长度为N，消息长度为K(BCH code generated polynomial, which codeword length N, message length K)

This file is contain a matlab file which describes

这是用身高体重数据进行性别分类的实验。 用最小错误率贝叶斯分类器决策时，首先通过比较概率大小判断一个体重身高二维向量代表的人是男是女，然后再逐一与已知性别的数据比较，就可以得到错误率的统计。然后改变先验概率，重复上面的过程，观察数据结果的变化。 用最小风险贝叶斯分类器决策时，首先求出用最小错误率贝叶斯分类器得到的条件概率；然后根据人为给定的决策表，根据公式算出条件风险；然后逐一比较条件风险，找出使条件风险最小的决策（也就是分类）。最后用分类得到的结果逐一比较已经知道的原始数据，统计处错误率。 (This is the height and weight data for gender classification experiment. With the minimum error rate Bayesian classifier decisions , first by comparing the probability of the size and weight to height to determine a person represented by two-dimensional vector is male or female , and then one by one with known gender data comparison, the statistical error rate can be . Then change the prior probability , repeat the above process , the results of the changes observed data . Bayesian classifier with the minimum risk decision-making , first find the minimum error rate using Bayesian classifier to get the conditional probability then artificially given decision table , according to the formula to calculate conditional risk and then one by one more conditional risk , to find ambassador to the conditions of minimum risk decision making (ie classification) . Finally, the results obtained with the classification by-side comparison of the raw data have been aware of SD error rate . )

bdpca face recgnition...............