Learning to classify a collection of images and texts
نویسندگان
چکیده
A single net system based on Kohonen’s Feature map was trained using a combined vector that contains visual features of an image and its collateral keywords. The performance of the single net was compared with a multinet system, comprising two SOMs, one trained with visual features and the other on keywords, in the presence of a Hebbian network that learns to associate visual features with keywords. The multi-net system performs better than the single net. Similar results were obtained when Grossberg’s ART networks were used instead of SOMs.
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