Search - Statistical Learning Toolbox

Search - Statistical Learning Toolbox - List

[Special Effects] sltoolbox_r101 DL : 1: sltoolbox (Statistical Learning Toolbox) organizes a comprehensive set of matlab codes in statistical learning, pattern recognition and computer vision. It includes 256 m-files in 24 categories, which are from low-level computational routines to high-level frameworks and algorithms
Update : 2008-10-13 Size : 4.92mb Publisher : 王冰
[Special Effects] sltoolbox_r101 DL : 0: sltoolbox (Statistical Learning Toolbox) organizes a comprehensive set of matlab codes in statistical learning, pattern recognition and computer vision. It includes 256 m-files in 24 categories, which are from low-level computational routines to high-level frameworks and algorithms
Update : 2025-02-17 Size : 4.92mb Publisher : 王冰
[matlab] stprtool DL : 0: 用matlab实现的统计模式识别工具箱，非常棒适合学习。-Achieved using statistical pattern recognition matlab toolbox, great for learning.
Update : 2025-02-17 Size : 4mb Publisher : 项龙江
[AI-NN-PR] LearningPatternClassificationASurvey DL : 0: 模式识别学习综述.该论文的英文参考文献为303篇.很有可读价值.Abstract— Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik–Chervonenkis theory, and neural networks. The presentation and the large (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists.-Summary of pattern recognition learning. The paper for English 303 references. Very readable value. Abstract-Classical and recent results in statistical patternrecognition and learning theory are reviewed in a two-classpattern classification setting. This basic model best illustratesintuition and analysis techniques while still containing the essentialfeatures and serving as a prototype for many applications.Topics discussed include nearest neighbor, kernel, and histogrammethods, Vapnik-Chervonenkis theory, and neural networks. Thepresentation and the large (thogh nonexhaustive) list of referencesis geared to provide a useful overview of this field for bothspecialists and nonspecialists.
Update : 2025-02-17 Size : 802kb Publisher : 蒋大为
[matlab] KStattoolbox DL : 0: 一个非常经典的核统计学习工具箱。集成了kpca、kdr、ksri等。具有分类和回归双重功能。-A very classic nuclear statistical learning toolbox. Integrated kpca, kdr, ksri and so on. Classification and regression with a dual function.
Update : 2025-02-17 Size : 224kb Publisher : 张强
[Special Effects] sltoolbox_r101 DL : 1: 统计学习工具箱，包括在统计学习，模式识别，计算机视觉方面的matlab程序。-Statistical Learning Toolbox organizes a comprehensive set of matlab codes in statistical learning, pattern recognition and computer vision.
Update : 2025-02-17 Size : 4.94mb Publisher : 杜雨
[Windows Develop] data DL : 0: SVM是支持向量机的缩写，是属于统计学习理论的一种人工智能算法。 osu svm是一个工具箱-SVM is a support vector machine stands are a statistical learning theory artificial intelligence algorithms. osu svm is a toolbox
Update : 2025-02-17 Size : 1.34mb Publisher : 盖瑞洋
[matlab] machine_learning_toolbox DL : 0: 这是个统计模式识别工具箱，是各种统计模式识别算法用matlab语言的实现。-The Statistical Pattern Recognition Toolbox is a collection of pattern recognition (PR) methods implemented in Matlab.
Update : 2025-02-17 Size : 6.64mb Publisher : 林箫
[matlab] Character-Recognition(Lib-SVM) DL : 0: 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 Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation.
Update : 2025-02-17 Size : 1.1mb Publisher : 任修齐
[AI-NN-PR] sltoolbox DL : 0: 统计学习工具箱，里面含有：图中相关算法，神经网络，支持向量机以及遗传算法等很多的统计学习算法。-Statistical Learning Toolbox
Update : 2025-02-17 Size : 4.97mb Publisher : liuanbenda
[AI-NN-PR] KStattoolbox DL : 0: 一个非常经典的核统计学习工具箱。集成了kpca,ksri等。具有分类和回归双重功能-A very classic nuclear statistical learning toolbox. Integrated kpca, kdr, ksri and so on. Classification and regression with a dual function.
Update : 2025-02-17 Size : 177kb Publisher : biebietuo
[matlab] GM_EM DL : 0: 不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional space. Note: This function requires the Statistical Toolbox and, if you wish to plot (for k = 2), the function error_ellipse Elementary usage: GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of clusters. Algorithm follows steps outlined in Bishop (2009) Pattern Recognition and Machine Learning , Chapter 9. Additional inputs: bn_noise- allow for uniform background noise term ( T or F , default T ). If T , relevant classification uses the (k+1)th cluster reps- number of repetitions with different initial conditions (default = 10). Note: only the best fit (in a likelihood sense) is returned. max_iters- maximum iteration number for EM algorithm (default = 100) tol- tolerance value (default = 0.01) Outputs idx- classification/labelling of data in X mu- GM centres
Update : 2025-02-17 Size : 3kb Publisher : 朱魏