Incorporating Prior Knowledge on Features into Learning

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How researchers see this paper

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Regularizing Text Categorization with Clusters of Words (2016, background): “Previous work (Ando and Zhang, 2005; Raina et al., 2006; Krupka and Tishby, 2007) shows such similarities can be inferred from prior domain knowledge and statistics computed on unlabeled data.”
A Group-Theoretic Approach to Abstraction: Hierarchical, Interpretable, and Task-Free Clustering (2018, background): “While a prior can take many forms such as rules in automatic reasoning, distributions in Bayesian inference, features in classiﬁers, or architectures in neural networks, it is typically task-speciﬁc and/or domain-speciﬁc (Raina et al., 2006; Yu et al., 2007; Krupka and Tishby, 2007).”
Regularized Learning with Networks of Features (2008, methodology): “As has been shown elsewhere [5, 6, 8], such similarities can be inferred from prior domain knowledge, auxiliary task learning, and statistics computed on unlabeled data.”
Model learning for robot control: a survey (2011, methodology): “Here, we need algorithms that allow us to improve the learning performance in the presence of sparse data, for example, by incorporating additional prior knowledge (Krupka and Tishby 2007; Schölkopf et al. 1997) or using active learning (Cohn et al.”