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We present a novel approach to learning predictive sequential models, called similarity-based alignment and generalization, which incorporates in the induction process a specific form of domain knowledge derived from a similarity function between the points in the input space. When applied to Hidden Markov Models, our framework yields a new class of learning algorithms called SimAlignGen. We discuss...
Most evaluation metrics in classification are designed to reward class uniformity in the example subsets induced by a feature (e.g., Information Gain). Other metrics are designed to reward discrimination power in the context of feature selection as a means to combat the feature-interaction problem (e.g., Relief, Contextual Merit). We define a new framework that combines the strengths of both kinds...
We introduce parallel collaborative programming-by-demonstration (PBD) as a principled approach to capturing knowledge on how to perform computer-based procedures by independently recording multiple experts executing these tasks and combining the recordings via a learning algorithm. Traditional PBD has focused on end-user programming for a single user, and does not support parallel collaborative procedure...
In this paper, we introduce a new approach to Programming-by-Demonstration in which the author is allowed to explicitly edit the procedure model produced by the learning algorithm while demonstrating the task. We describe Augmentation-Based Learning, a new algorithm that supports this approach by considering both demonstrations and edits as constraints on the hypothesis space, and resolving conflicts...
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