Relevance-Vector-Machine Quantization and Density-Function Estimation: Application to HMM-Based Multi-Aspect Target Classification
نویسندگان
چکیده
The relevance vector machine (RVM) is applied for feature-vector quantization (codebook design) and for density-function estimation in high-dimensional feature space. The RVM represents a Bayesian extension of the widely applied support vector machine (SVM). The use of RVMs for quantization and density-function estimation is explored with application to discrete and continuous HMMs, respectively, with comparisons provided to traditional Lloyd codebook design and Gaussian-mixturemodel density-function estimation. The RVM-HMM algorithm is employed for multiaspect target classification, although such is of general utility in HMM applications. Example results are presented for measured multi-aspect acoustic scattering data from several underwater elastic targets.
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