搜索资源列表
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6下载：
统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines,
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SVM的VC程序，有matlab接口，便于调用，还有例子，用于模式识别，分类，预测。-SVM, VC procedures, matlab interface, easy call, as well as an example, for pattern recognition, classification and prediction
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SVM分类器，用于对多维采样点进行分类。可根据类别数修改分类器，我们的模式识别作业。-SVM classifier, multi-dimensional sampling points used for classification. Can be modified according to the number of classification categories, and our pattern recognition operation.
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支持向量机的matlab程序实现，可用于模式分类，模式识别-SVM matlab program can be used for pattern classification, pattern recognition
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关于支持向量机的MATLAB程序，用于模式识别-On the MATLAB support vector machine procedures for pattern recognition
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模式识别基本方法matlab源代码，包括最小二乘法、SVM、神经网络、1_k近邻法、剪辑法、特征选择和特征变换。-Basic method of pattern recognition matlab source code, including the least squares method, SVM, neural network, 1_k neighbor method, editing method, feature selection and feature transformatio
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stprtool的使用文档，非常好的模式识别工具包，里面有关于核的以及贝叶斯的相关程序，以及PCA，EM，SVM等方法。 -stprtool the use of the document, very good pattern recognition tool kit, which has nuclear as well as Bayes procedures, as well as PCA, EM, SVM and other methods.
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在ORL或Yale标准人脸数据库上完成模式识别任务。用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验.
-Or Yale in the ORL face database, complete the standard pattern recognition tasks. With the PCA and kernel-based PCA (KPCA) method to complete the reconstruction of face image and reco
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模式识别中常用的算法，有感知机算法，支撑矢量机算法和LS算法，并附有实验报告，源程序！-Commonly used pattern recognition algorithms, has perceptron algorithm, support vector machine algorithm and the LS algorithm, along with lab reports, source code!
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SVM分类器,关键是对信号进行分类，是模式识别的必备宝典-SVM classifier, the key is to classify the signal is essential for pattern recognition Collection
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MATLAB函数参考手册，查看matlab函数作用以及功能。- SVMLSPex02.m
Two Dimension SVM Problem, Two Class and Separable Situation
Difference with SVMLSPex01.m:
Take the Largrange Function (16)as object function insteads ||W||,
so it need more
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支持向量机的研究现已成为机器学习领域中的研究热点，其理论基础是Vapnik[3]等提出的统计学习理论。统计学习理论采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型泛化误差的上界，即最小化模型的结构风险，从而提高了模型的泛化能力，这一优点在小样本学习中更为突出。SVM理论正是在这一基础上发展而来的，经过十几年的研究和发展，已开始逐步应用于一些领域。在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，已经在模式识别、函数逼近和概率密度估计等方面取得了良好的效果。- Support
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增量SVM的实例，用于对手势的在线增量学习，有很详细的源码，并实现了SVM的多分类问题，对于模式识别和学习SVM有很大的帮助-Instance of the incremental SVM for gesture online incremental learning, there is a very detailed source, and to achieve a multi-SVM classification, pattern recognition and learning SVM g
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模式识别，线性分类器。经调试可用，希望可以帮到需要的人。-Pattern recognition, linear classifier. After debugging is available, the desire to help people in need.
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模式识别课程作业，em算法，svm算法，适合初学者，绝对简单例子-Pattern Recognition course work, em algorithm, svm algorithm, suitable for beginners, absolutely simple example
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模式识别中knn和svm的使用，用到matlab的fitcknn和fitcsvm-pattern recognition the use of knn and svm to classify data, using matlab function fitcknn and fitcsvm
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支持向量机（SVM）是模式识别中很常用的方法，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势-Support Vector Machine (SVM) is a very common pattern recognition methods in solving the small sample, nonlinear and high dimensional pattern recognition exhibit many unique advantages
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支持向量机的仿真程序简单案例分析（分类和预测）模式识别-SVM simulation program simple case analysis (classification and prediction) Pattern Recognition
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将支持向量机（SVM）用于模式识别，解决二分类问题，程序中包含训练集和测试集。(The support vector machine (SVM) is used for pattern recognition to solve the dichotomy problem, which includes training set and test set.)
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pattern recognition using fractal lacunarity and svm
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