crclib

code_ICCV2009_multiobject_context,ICCV2009年的多目标文本的程序(code_ICCV2009_multiobject_context, ICCV2009 multi-year program target text)

遗传量子算法求解背包问题Matlab源码(Quantum Genetic Algorithm for Knapsack Problem)

三维路径规划算法，蚁群算法实现，注释详细清晰，程序完整(Three-dimensional path planning algorithm, ant colony algorithm, detailed notes clear, complete procedures)

new method for noisy enviroment fuzzy adaptation

模式识别中txt数据的导入算法，大家看看，做个参考(Pattern recognition algorithms in the txt data import, we look to be a reference)

利用Capon, MUSIC进行窄带信号空域信号方向估计。比较分辨率性能。观察阵元数、信噪比、快拍数对估计器性能的影响。 (Using Capon, MUSIC for narrowband signal airspace signal direction estimates. Comparative resolution performance. Observe the number of array elements, signal to noise ratio, the number of snapshots on the estimator performance.)

chebyshev 2值量化辛辛苦苦的编程 奉献给大家 ，愿大家在混沌序列上共同提高(chebyshev 2 value quantization laboriously programming dedicated to you, everyone is willing to jointly improve the chaotic sequence)

基于多相结构的信道化接收机，包含特征值与特征向量的提取、训练样本以及最后的识别，自写曲率计算函数 。( Channelized receiver based on multi-phase structure, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, Since writing the curvature calculation function.)

这个是最小二乘算法的一个重要用途，在实际操作中有广泛的应用，可以处理很多问题！(This is an important use of least squares algorithm, in practice a wide range of applications, can handle a lot of problems!)

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 . )