nz.ac.waikato.cms.weka » votingFeatureIntervals
Classification by voting feature intervals. Intervals are constucted around each class for each attribute (basically discretization). Class counts are recorded for each interval on each attribute. Classification is by voting. For more info see: G. Demiroz, A. Guvenir: Classification by voting feature intervals. In: 9th European Conference on Machine Learning, 85-92, 1997.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » wavelet
A filter for wavelet transformation. For more information see: Wikipedia (2004). Discrete wavelet transform. Kristian Sandberg (2000). The Haar wavelet transform. University of Colorado at Boulder, USA.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » winnow
Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318; N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz. Does classification for problems with nominal attributes (which it converts into binary attributes)
更新时间: 1970-01-01 08:00| 仓库 | 个数 |
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