Hidden-Unit Conditional Random Fields 工具箱，可以用于训练linearCRF和和L.J.P. van der Maaten, M. Welling 提出的huCRF-We provide Matlab code that implements the training and evaluation of hidden-unit CRFs, as well as code to reproduce the results of our experim

模式识别是对样本进行聚类，感知器算法是通过迭代计算修正权向量，使样本满足条件，从而实现分类。本程序对感知器算法进行了改进，当权向量不满足时，立即退出此轮计算，进入下一轮迭代，从而减少了计算次数。程序对代码有详细注释，对样本的个数和维数自动判别。-Pattern recognition, clustering samples, perception algorithm is iterative correction weight vector samples meet the conditions

感知机 代码，源数据为五维，前四为输入数据，最后一列为分类-Perceptron code, the source data for the five-dimensional, the first four of the input data, the last one listed as Category

模式识别中的感知器判别准则，实现分类。Matlab源代码-Pattern Recognition Perceptron criterion and achieve classification. Matlab source code