Maximum entropy classification applied to speech

被引:0
|
作者
Gupta, M [1 ]
Friedlander, MP [1 ]
Gray, RM [1 ]
机构
[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We present a new method for classification using the maximum entropy principle(1), allowing full use of relevant training data and smoothing the data space. To classify a test point we compute a maximum entropy weight distribution over a subset of training data and constrain the weights to exactly reconstruct the test point. The classification problem is formulated as a linearly constrained optimization problem and solved using a primal-dual logarithmic barrier method, well suited for high-dimensional data. We discuss theoretical advantages and present experimental results on vowel data which demonstrate that the method performs competitively for speech classification tasks.
引用
收藏
页码:1480 / 1483
页数:4
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