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To reduce the human effort in labeling the training set for document classification, some learning algorithms ask users to give the representative keywords for each class rather than any labeled documents. The key challenge in such \emph {keyword-labeled classification} is how to learn the high quality classifier with
MRF related AIA approach; we explore the optimal parameter estimation and model inference systematically to leverage the learning power of traditional generative model. Specifically, we propose new potential function for site modeling based on generative model and build local graphs for each annotation keyword. The
information can aid the learning process given a fixed amount of labeled images. In particular, we consider a scenario where keywords are associated with the training images, e.g. as found on photo sharing websites. The goal is to learn a classifier for images alone, but we will use the keywords associated with labeled and
This article presents an intelligent fuzzy rule-based approach for computing optimal set of joints torques, for manipulating a grasped object by a dexterous multi-fingered robotics hand. The intelligent approached followed here, is to let a learning fuzzy system to approximate a nonlinear force formulation for optimal contact forces. This has been achieved via following two major steps: The first...
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