{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/43074"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/43074","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A context-sensitive meta-classifier for color-naming","abstract":"Humans are sensitive to situational and semantic context when applying labels to colors. This is especially challenging for algorithms which attempt to replicate human categorization for communicative tasks. Additionally, mismatched color models between dialog partners can lead to a back-and-forth negotiation of terms to find common ground. This thesis presents a color-classification algorithm that takes advantage of a dialog-like interaction model to provide fast-adaptation for a specific exchange. The model learned in each exchange is then integrated into the system as a whole. 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