论文信息 - Weka: Practical machine learning tools and techniques with Java implementations

Weka: Practical machine learning tools and techniques with Java implementations

The Waikato Environment for Knowledge Analysis (Weka) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning and data mining algorithms. Weka is freely available on the World-Wide Web and accompanies a new text on data mining [1] which documents and fully explains all the algorithms it contains. Applications written using the Weka class libraries can be run on any computer with a Web browsing capability; this allows users to apply machine learning techniques to their own data regardless of computer platform.

[1] Larry A. Rendell,et al. A Practical Approach to Feature Selection , 1992, ML.

[2] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.

[3] David W. Aha,et al. Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[4] Ian H. Witten,et al. Induction of model trees for predicting continuous classes , 1996 .

[5] Lloyd A. Smith,et al. Practical feature subset selection for machine learning , 1998 .

[6] Keki B. Irani,et al. Multi-interval discretization of continuos attributes as pre-processing for classi cation learning , 1993, IJCAI 1993.

[7] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.

[8] Aiko M. Hormann,et al. Programs for Machine Learning. Part I , 1962, Inf. Control..

[9] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[10] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .

[11] Tomasz Imielinski,et al. Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[12] Y. Freund,et al. Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .