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- W1569335651 abstract "Neural networks (NN) are alternate tools for classification tasks. They have great generalization ability and work well for many real-world problems, such as acoustic-phonetic decoding in a speech recognition system. This chapter presents and evaluates a new version of the learning vector quantization (LVQ) algorithm called dynamic vector quantization (DVQ). The new method is based on an incremental procedure. DVQ consists in starting the training phase with only one reference vector per class and increasing the number of references progressively. To compare DVQ to some other classifiers, two classification problems on synthetic data are studied and an evaluation is performed on a multispeaker isolated letters data base. Synthetic data with known statistical distributions allow to benchmark DVQ classifier with few others and to compare to the theoretical limit of the Bayes classifier. Artificial data correspond to difficult classification tasks with heavily overlapping Gaussian distributions and high dimensionality." @default.
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- W1569335651 date "1991-01-01" @default.
- W1569335651 modified "2023-10-18" @default.
- W1569335651 title "DVQ: DYNAMIC VECTOR QUANTIZATION - AN INCREMENTAL LVQ" @default.
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- W1569335651 doi "https://doi.org/10.1016/b978-0-444-89178-5.50082-8" @default.
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