An analytical relation between analogical modeling and memory based learning
نویسندگان
چکیده
Analogical modeling (AM) is a memory based model. Known algorithms implementing AM depend on investigating all combinations of matching features, which in the worst case is exponential (O(2)). We formulate a representation theorem on analogical modeling which is used for implementing a range of approximations to AM with a much lower complexity. We will demonstrate how our model can be modified to reach better performance than the original AM model a popular categorization task (chunk tagging).
منابع مشابه
Efficient Modeling of Analogy
Analogical modeling (AM) is a memory based model with a documented performance comparable to other types of memory based learning. Known algorithms implementing AM have a computationally complexity of O(2). We formulate a representation theorem on analogical modeling which is used for implementing a range of approximations to AM with a complexity starting as low as O(n).
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