Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance.

翻译：诱导法从有用的前科中受益。 惩罚性回归法,如山脊回归法,将重量缩到零但零关联法,通常不是明智的先行方法。 受简单而稳健的决策超自然人类使用的启发,我们建造了非零前科,为惩罚性的回归模型提供了可靠和可解释的多种任务解决方案。 我们的方法使受限制模型的估计数能够作为更普遍的模型的先导,从而产生一种在复杂程度不同的模型之间进行内插的原则性方法。 我们成功地将这一方法应用于一些决策和分类问题,并分析模拟的脑成像数据。 强健的前科模型具有极坏的性能。 这些新的算法可以用于数据分析和机器学习,并有助于理解人们如何从新手法过渡到专家性能。