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parzen
- 分类器的训练与学习是模式识别的一个重要环节,其目的在于按照某种算法,确定判决规则,使之具有自动分类识别的能力。本文介绍了采用Parzen窗法的随机模式分类器,并matlab实现了一个简易的随机模式分类器。-Classifier training and learning is an important part of pattern recognition, in accordance with the purpose of some kind of algorithm to determine
classifier
- 两类二维相关正态分布条件下的最小错误率贝叶斯分类器，基于最小风险的贝叶斯分类器，Parzen窗法非参数估计分类器程序，Fisher线性判别法分类器程序。-Under normal conditions two types of two-dimensional correlation of minimum error rate of Bayesian classifier, the minimum risk-based Bayesian classifier, Parzen window meth
Ex1
- 模式识别某次课程的作业，完成了高斯分布下的两种贝叶斯分类器,以及非参数的K近邻、Parzen窗方法，采用UCI机器学习数据库中的某些数据作为样本，使用交叉验证方法确定参数-Pattern recognition of a particular course work, completed under the two Gaussian Bayesian classifier, and the non-parametric K-nearest neighbor, Parzen window meth
PatterRecognition-4.0
- 模式识别 作业 实现自动产生样本，并用最近距离法，贝叶斯分类，Parzen窗概率密度估计-Pattern recognition operations automatically generate the sample, and with the recent distance method, Bayesian classifier, Parzen window probability density estimation
T-HOMEWORK
- 用Parzen窗法或者kn近邻法估计概率密度函数，得出贝叶斯分类器，对测试样本进行测试，比较与参数估计基础上得到的分类器和分类性能的差别.2． 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。3．选择上述或以前实验的任意一种方法，用留一法在训练集上估计错误率，与在测试集上得到的错误率进行比较。-Use Parzen Wi
NBC
- naive bayes classifier with parzen window kernel fitting
classify_nn
- This little package contains a Parzen Neural Network classifier that can classify data between N classes in D dimensions. The classifier is really fast and simple to learn
DBSCAN
- This little package contains a Parzen Neural Network classifier that can classify data between N classes in D dimensions. The classifier is really fast and simple to learn
q1
- This little package contains a Parzen Neural Network classifier that can classify data between N classes in D dimensions. The classifier is really fast and simple to learn
LabviewExample4LIBSVM
- This little package contains a Parzen Neural Network classifier that can classify data between N classes in D dimensions. The classifier is really fast and simple to learn
svm1
- This little package contains a Parzen Neural Network classifier that can classify data between N classes in D dimensions. The classifier is really fast and simple to learn