Classifying dialogue in high-dimensional space

The richness of multimodal dialogue makes the space of possible features required to describe it very large relative to the amount of training data. However, conventional classifier learners require large amounts of data to avoid overfitting, or do not generalize well to unseen examples. To learn di...

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Veröffentlicht in:	ACM transactions on speech and language processing 2011-05, Vol.7 (3), p.1-15
Hauptverfasser:	González-Brenes, José P., Mostow, Jack
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Children Classifiers Data points Displays Finishes Regression Trains
Online-Zugang:	Volltext
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