Description: 统计学习理论和SVM(支持向量机).ppt入门讲义之一-statistical learning theory and SVM (support vector machine). Ppt s one of the portal Platform: |
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Author:zhoujx |
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Description: 对支持向量机和统计学习理论作了较系统的综述-on support vector machines and statistical learning theory made more systematic overview Platform: |
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Author:lunar1983 |
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Description: 统计学习本质论，支持向量机（svm）的经典书籍-nature of statistical learning theory and support vector machines (svm) of the classic books Platform: |
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Author:chenxu |
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Description: 最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, \"The Nature of Statistical Learning Theory\", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, \"Fast training of support vector machines using sequential minimal optimization\", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, \"Estimating the Generalization Performance of a SVM Efficiently\", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it. Platform: |
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Author:金星 |
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Description: 最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it. Platform: |
Size: 172032 |
Author:金星 |
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Description: 统计学习理论和SVM(支持向量机).ppt入门讲义之一-statistical learning theory and SVM (support vector machine). Ppt s one of the portal Platform: |
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Author:zhoujx |
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Description: 对支持向量机和统计学习理论作了较系统的综述-on support vector machines and statistical learning theory made more systematic overview Platform: |
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Author:lunar1983 |
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Description: 统计学习本质论，支持向量机（svm）的经典书籍-nature of statistical learning theory and support vector machines (svm) of the classic books Platform: |
Size: 4871168 |
Author:chenxu |
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Description: 本文主要讲了统计学习理论的本质，对学习支持向量机的学习有很大帮助。-This article talked about the nature of statistical learning theory, support vector machine to study the study of much help. Platform: |
Size: 4658176 |
Author:xiaoying |
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Description: 支持向量机作为统计学习理论的实现方法，能很好地解决非线性和高维数问题，克服了神经网络方法收敛慢、解不稳定、推广性差的缺点，近年来得到了广泛地研究，在模式识别、信号处理、控制、通讯等方面得到了广泛地应用。-Support Vector Machine as the implementation of statistical learning theory approach, can be a good solution to the nonlinear and high dimension problem, the neural network method to overcome the slow convergence, solution instability, bad promotion of sexual shortcomings, in recent years has been widely studied, at pattern recognition, signal processing, control, communications, etc. has been widely applied. Platform: |
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Author:王旭 |
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Description: 支持向量机（SVM）是在统计学习理论的基础上发展起来的新一代学习算法，该书可以帮助初学者学习SVM。-Support Vector Machine (SVM) is a statistical learning theory developed on the basis of a new generation of learning algorithm, which can help beginners learn SVM. Platform: |
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Author:蒲秀娟 |
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Description: 《统计学习理论与支持向量机》，清华张学工教授发表在自动化学报的文章-" Statistical Learning Theory and Support Vector Machine," Tsinghua University Professor Zhang Xuegong articles published in Acta Automatica Sinica Platform: |
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Author:chiang |
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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: |
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Author:NBB |
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Description: 此文章是介绍支持向量机的经典书籍，对使用支持向量机的同行们很有帮助-This article is to introduce the support vector machine classic books on the use of support vector machines helpful colleagues Platform: |
Size: 10842112 |
Author:绿叶 |
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Description: 统计学习理论 Vapnik
本书是对统计学习理论和支持向量机方法的全面、系统、详尽的阐述，是各领域中研究和应用机器学习理论与方法的科研工作者和研究生的重要参考资料。-Statistical learning theory Vapnik book of statistical learning theory and support vector machine method of comprehensive, systematic and detailed exposition of the various fields of research and application of theories and methods of machine learning researchers and graduate students an important reference. Platform: |
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Description: 而支持向量机是90 年代中期发展起来的基于统计学习理论的一种机器学习方法，通过寻求结构化风险最小来提高学习机泛化能力-The support vector machine is a machine learning method based on statistical learning theory developed in the mid-1990s, the minimum to improve the generalization ability of learning by seeking a structured risk Platform: |
Size: 1083392 |
Author:yangfeng |
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Search - support vector statistical learning theory