Statistical-Learning-Theory The goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.
标签： statistical Statistical-Learning-Theory learning theory
上传时间： 2017-07-15
上传用户：363186
Support Vector Machine is small sample method based on statistic learning theory. It is a new method to deal with the highly nonlinear classification and regression problems .It can better deal with the small sample, nonlinear and
标签： method statistic learning Support
上传时间： 2014-12-02
上传用户：zukfu
机器学习经典书籍The Elements of Statistical Learning--Data Mining, Inference and Prediction. 作者：Friedman
标签： Statistical Prediction Inference Elements
上传时间： 2014-12-03
上传用户：奇奇奔奔
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.
上传时间： 2013-12-16
上传用户：亚亚娟娟123
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.
标签： statistical Classical Abstract pattern
上传时间： 2013-11-25
上传用户：www240697738
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
标签： Introduction Classifiers Algorithms introduces
上传时间： 2015-10-20
上传用户：aeiouetla
YASMET: Yet Another Small MaxEnt Toolkit (Statistical Machine Learning) 由Franz Josef Och编写，一个简短但非常经典的最大熵统计模型实现源码。
标签： Statistical Learning Another Machine
上传时间： 2015-11-17
上传用户：xiaodu1124
Machine Learning, Neural and Statistical Classification Editors: D. Michie, D.J. Spiegelhalter, C.C. Taylor February 17, 1994
标签： C. D. D.J. Classification
上传时间： 2015-12-14
上传用户：日光微澜
Information theory, inference and learning algorithms
标签： 编码
上传时间： 2016-04-12
上传用户：baiyouren
Bayes networks. From theory to application. E book document for advanced bayes theory and statistical model
标签： theory application statistica networks
上传时间： 2013-12-20
上传用户：三人用菜
