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10Algorithms-08
- This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
10Algorithms-08
- This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
manual_stprtool
- 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.
Chinese-text-categorization-Study
- 本文通过对Bayes、KNN、SVM 应用于中文文本分类进行比较实验研究。 应用ICTCLAS 对中文文档进行分词，在大维数，多数据情况下应用TFIDF 进行 特征选择，并同时利用它实现了对特征项进行加权处理，使文本库中的每个文本 具有统一的、可处理的结构模型。然后通过三类分类算法实现了对权值数据进行 训练和分类。-Based on the Bayes, KNN, SVM applied to compare the Chinese text ca
SVM
- 朴素贝叶斯 和 svm实现，其中采用svmlib的java实现-bayes java
stprtool22oct09
- 是一个模式识别工具包，里面包含bayes,linear分类函数，还有支持向量机SVM工具包-It is a pattern recognition toolbox, which contains the bayes, linear classification function, as well as support vector machines SVM toolbox
10-da--suanfa
- 讲述了最著名的十大数据挖掘算法，经典资料，国际权威的学术组织the IEEE International Conference on Data Mining (ICDM) 2006年12月评选出了数据挖掘领域的十大经典算法：C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.-About the top ten most famous data mining algorithms, the
dataming
- 介绍数据挖掘的10种主要算法及其应用 一种透过数理模式来分析企业内储存的大量资料，以找出不同的客户或市场划分，分析出消费者喜好和行为的方法。 -Top 10 algorithms in data mining his paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006:
classification
- 分类算法，包括KNN，SVM,Linear Regression, Naive Bayes四种-four algorithms of classification, includes KNN, SVM, Linear Regression, Naive Bayes
Text-classify
- 用朴素贝叶斯和支持向量机SVM的两种文本文类实验-Two text of this article class experiment with naive Bayes and support vector machines SVM
machine-learning-5
- 机器学习算法之SVM与朴素贝叶斯，经典的机器学习的外文资料，该资料描述详细，便于大家的学习。-Machine learning algorithms of SVM and Naive Bayes, classical machine learning foreign language information, the information described in detail, easy to learn from everyone.
Machine-Learning
- 机器学习的讲义和作业，包括了SVM、隐氏马尔科夫和朴素贝叶斯等方法，非常适合初学机器学习的人！-Machine learning lectures and assignments, including SVM, Hidden Markov and Naï ve Bayes methods, machine learning is ideal for beginners！
stprtool_v2.12
- 统计模式识别工具箱(STPRTool 版本2.12 2013-09-12) 功能有线性判别函数、特征提取、密度估计和聚类、支持向量机、贝叶斯分类器、交叉验证等-Statistical Pattern Recognition Toolbox Methods: Fisher,PCA,GMM,K-means,SVM,Bayes classifier,Cross-validation,KNN,etc.
eMailSystem
- 采用有监督的朴素贝叶斯、SVM和KNN算法对进行训练，实现对邮件的分类-Using supervised naive bayes, SVM and KNN algorithm for training, implementation of the classification of the mail
src
- Opninion mining project for Movie Review and Performance ANalysis Between Tow Algorithm Naive bayes and SVM
wvnwtcsv
- 连续相位调制信号(CPM)产生，包括最小二乘法、SVM、神经网络、1_k近邻法,模式识别中的bayes判别分析算法，一个很有用的程序，现代信号处理中谱估计在matlab中的使用。- Continuous phase modulation signal (CPM) to produce, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Pattern Recognition ba
prxertqg
- 正确率可以达到98％，仿真效率很高的，IMC-PID是利用内模控制原理来对PID参数进行计算，各种kalman滤波器的设计，包括最小二乘法、SVM、神经网络、1_k近邻法,通过反复训练模板能有较高的识别率，可以得到很精确的幅值、频率、相位估计，模式识别中的bayes判别分析算法。- Accuracy can reach 98 , High simulation efficiency, The IMC- PID is using the internal model control princip
sjqxybsb
- 包括最小二乘法、SVM、神经网络、1_k近邻法,一些自适应信号处理的算法，基于互功率谱的时延估计，有CDF三角函数曲线/三维曲线图，针对EMD方法的不足,数据包传送源码程序，模式识别中的bayes判别分析算法，LDPC码的完整的编译码。- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Some adaptive signal processing algorithms, Ba
SVM-and-NB
- 支持向量机与朴素贝叶斯算法，对数据进行分类后深度了解数据的结构-Support vector machine and naive Bayes algorithm.Classifying the data and understanding the structure of the data in depth
weka机器学习十大算法
- 对机器学习领域的十个经典算法进行了详细介绍，包括：AdaBoost、Apriori、C4.5、CART、EM、K-means、kNN、PageRand、SVM和朴素贝叶斯(Ten classical algorithms in machine learning domain are introduced in detail, including AdaBoost, Apriori, C4.5, CART, EM, K-means, kNN, PageRand, SVM and Nave Baye