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.
標簽： statistical StatistiCAl-Learning-Theory learning theory
上傳時間： 2017-07-15
上傳用戶：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
標簽： method statistic learning Support
上傳時間： 2014-12-02
上傳用戶：zukfu
機器學習經典書籍The Elements of Statistical Learning--Data Mining, Inference and Prediction. 作者：Friedman
標簽： Statistical Prediction Inference Elements
上傳時間： 2014-12-03
上傳用戶：奇奇奔奔
最新的支持向量機工具箱，有了它會很方便 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.
上傳時間： 2013-12-16
上傳用戶：亞亞娟娟123
模式識別學習綜述.該論文的英文參考文獻為303篇.很有可讀價值.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.
標簽： statistical Classical Abstract pattern
上傳時間： 2013-11-25
上傳用戶：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.
標簽： Introduction Classifiers Algorithms introduces
上傳時間： 2015-10-20
上傳用戶：aeiouetla
YASMET: Yet Another Small MaxEnt Toolkit (Statistical Machine Learning) 由Franz Josef Och編寫，一個簡短但非常經典的最大熵統計模型實現源碼。
標簽： Statistical Learning Another Machine
上傳時間： 2015-11-17
上傳用戶：xiaodu1124
Machine Learning, Neural and Statistical Classification Editors: D. Michie, D.J. Spiegelhalter, C.C. Taylor February 17, 1994
標簽： C. D. D.J. Classification
上傳時間： 2015-12-14
上傳用戶：日光微瀾
Information theory, inference and learning algorithms
標簽： 編碼
上傳時間： 2016-04-12
上傳用戶：baiyouren
Bayes networks. From theory to application. E book document for advanced bayes theory and statistical model
標簽： theory application statistica networks
上傳時間： 2013-12-20
上傳用戶：三人用菜
