| 摘要 |
Methods and apparatus for producing efficiently sized models suitable for pattern recognition purposes are described. Various embodiments are directed to the automated generation, evaluation, and selection of reduced size models from an initial model having a relatively large number of components, e.g., more components than can be stored for a particular intended application. To achieve model size reduction in an automated iterative manner, expectation maximization (EM) model training techniques are combined, in accordance with the present invention, with model size constraints. In one embodiment, a new reduced size model is generated using a LaGrange multiplier from an input model and input size constraints during each iteration of the size reducing model training process. The reduced size model generated during one iteration of the process serves as the input to the next iteration. Scoring, e.g., maximum likelihood scoring, and evaluation steps, in conjunction with stop criteria, are used to determine the number of model size reducing iterations performed, and which reduced size model is selected as the output of the size reduction model training process.
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