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A least mean-squared error approach to syntactic classification
1Department of Mathematics, Arizona State University, Tempe, AZ 85281.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study introduces a novel method for syntactic pattern recognition, moving beyond traditional probability estimation. It proposes using a least mean square error (LMSE) discriminant hyperplane in a structural index space derived from context-free grammars.
Area of Science:
- Computational Linguistics
- Pattern Recognition
- Machine Learning
Background:
- Syntactic pattern recognition traditionally relies on estimating production probabilities of stochastic context-free grammars.
- This probabilistic approach can be computationally intensive and may not always yield optimal discriminative power.
Purpose of the Study:
- To propose an alternative numerical training approach for syntactic pattern recognition.
- To explore the effectiveness of a discriminant hyperplane in a structural index space.
Main Methods:
- Developed a method based on finding a least mean square error (LMSE) discriminant hyperplane.
- Utilized a feature space defined by 'structural indices' derived from context-free grammars.
- Class samples are mapped into this structural index space for classification.
Main Results:
- Demonstrated a new approach to syntactic pattern recognition that bypasses direct probability estimation.
- The LMSE hyperplane provides a discriminative boundary in the space of structural indices.
Conclusions:
- The proposed LMSE discriminant hyperplane method offers a viable alternative for numerical training in syntactic pattern recognition.
- This approach may offer advantages in certain pattern recognition tasks by focusing on discriminative features rather than generative probabilities.
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