| 摘要 |
A method is provided for training a statistical pattern recognition decoder on new data while preserving its accuracy of old, previously learned data. Previously learned data are represented as constrained equations that define a constrained domain (T) in a space of statistical parameters (K) of the decoder. Some part of a previously learned data is represented as a feasible point on the constrained domain. A training procedure is reformulated as optimization of objective functions over the constrained domain. Finally, the constrained optimization functions are solved. This training method ensures that previously learned data is preserved during iterative training steps. While an exemplary speech recognition decoder is discussed, the inventive method is also suited to other pattern recognition problems such as, for example, handwriting recognition, image recognition, machine translation, or natural language processing.
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