Description: uci机器学习数据库关于字符识别的源数据。是字符识别的研究的关键数据来源。包括手写字体和印刷体两类，手写的数字和印刷体的字母。-uci machine learning database on the source character recognition data. Character recognition research is the key source of data. Including two types of handwriting fonts, and print, handwritten numbers and letters in print. Platform: |
Size: 875520 |
Author:许翔 |
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Description: 图模型（Graphical Model）,加州大学柏克利计算机系、统计系教授，Jordan的技术报告，适合于机器学习、概率统计、贝叶斯网络等的研究-Graphical Model, University of California at Berkeley ,Dept. of computer science, Dept. of statistics, Professor Jordan s technical report, suitable for machine learning, probability and statistics, Bayesian networks, etc Platform: |
Size: 370688 |
Author:yism |
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Description: .Sparse coding algorithm.We can also apply it onefficient sparse coding algorithm to a new machine learning framework called "self-taught learning", where we are given a small amount of labeled data for a supervised learning task, and lots of additional unlabeled data that does not share the labels of the supervised problem and does not arise from the same distribution. Platform: |
Size: 2048 |
Author:huneza |
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Description: 這是一個 Matlab的（和獨立應用）端口的出色的機器學習算法，隨機森林` - 由Leo布賴曼等。從 R -源由Andy鴻等。 http://cran.r-project.org/web/packages/randomForest/index.html（Fortran的原由Leo布賴曼和Adele卡特勒，研究港口安迪鴻和馬修維納。）當前的代碼版本是基於 4.5-29從來源 randomForest包。-This is a Matlab (and Standalone application) port for the excellent machine learning algorithm `Random Forests - By Leo Breiman et al. from the R-source by Andy Liaw et al. http://cran.r-project.org/web/packages/randomForest/index.html ( Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.) Current code version is based on 4.5-29 from source of randomForest package. Platform: |
Size: 523264 |
Author:Rong |
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Description: 一些非常有用的数据集，适合我们从事机器学习的人使用，matlab下的mat格式和excel格式，包括注明的iris，糖尿病等数据集-some useful datasers for machine learning learners,such as diabeters,iris and so on Platform: |
Size: 301056 |
Author:王凯 |
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Description: 增强学习Matlab源吗,对机器学习,在线控制的人有所帮助-Matlab source you enhance the learning of machine learning, online help people control Platform: |
Size: 1024 |
Author:David |
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Description: Learning Automata Algorithms
A collection of machine learning programs which contains some of Learning Automata algorithms such as Tsetline, Krinisky, Krylov, LRI, LIP, LRR, LReP, SoftMax, SampleAverage and Random strategy. All of these developed in MATLAB 6.5 Platform: |
Size: 9216 |
Author:Aso |
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Description: 香蕉形(banana)标准数据集，用于测试机器学习与模式识别算法。-Banana-shaped standard data set for testing machine learning and pattern recognition algorithm. Platform: |
Size: 15360 |
Author:何创新 |
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Description: 机器学习中关于朴素贝叶斯（NB）方法的Matlab源代码，附带四个数据集用于实验-Machine Learning on Bayesian (NB) method of Matlab source code, with four data sets for experiments Platform: |
Size: 1489920 |
Author:文瑞 |
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Description: 优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。-first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized Platform: |
Size: 334848 |
Author:NBB |
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Description: 高斯过程算法在回归和分类中的应用程序。与书本《基于高斯过程的机器学习》配套。本程序是最新的v3.1版，更新于2010-09-27-Gaussian process regression and classification algorithm in the application. And the book " machine learning based on Gaussian process" support. This program is the latest v3.1 version, updated on 2010-09-27 Platform: |
Size: 609280 |
Author:hu |
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Description: 机器学习中关于分类的一段matlab程序，自己的课后大作业，是关于手写数字识别-Machine learning matlab on a classified program, their major job after school, on the handwritten numeral recognition Platform: |
Size: 90112 |
Author:陈楠 |
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Description: Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.-Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves. Platform: |
Size: 6144 |
Author:Gang Fu |
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Description: 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. Platform: |
Size: 1155072 |
Author:任修齐 |
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