منابع مشابه
Online Multitask Learning
We study the problem of online learning of multiple tasks in parallel. On each online round, the algorithm receives an instance and makes a prediction for each one of the parallel tasks. We consider the case where these tasks all contribute toward a common goal. We capture the relationship between the tasks by using a single global loss function to evaluate the quality of the multiple predictio...
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In this paper, we propose an online multitask learning framework where the weight vectors are updated in an adaptive fashion based on inter-task relatedness. Our work is in contrast with the earlier work on online multitask learning (Cavallanti et al., 2008) where the authors use a fixed interaction matrix of tasks to derive (fixed) update rules for all the tasks. In this work, we propose to up...
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Relative similarity learning (RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with realworld data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-o...
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Introduction: Most online learning environments are challenging for the design of collaborative learning activities to achieve high-level learning skills. Therefore, the purpose of this study was to design and validate a model for collaborative learning in online learning environments. Methods: The research method used in this study was a mixed method, including qualitative content analysis and...
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In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2014
ISSN: 1041-4347
DOI: 10.1109/tkde.2013.139