{"id":150990,"date":"1991-01-01T00:00:00","date_gmt":"1991-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/quantifying-the-value-of-constructive-induction-knowledge-and-noise-filtering-on-inductive-learning\/"},"modified":"2018-10-16T20:15:33","modified_gmt":"2018-10-17T03:15:33","slug":"quantifying-the-value-of-constructive-induction-knowledge-and-noise-filtering-on-inductive-learning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/quantifying-the-value-of-constructive-induction-knowledge-and-noise-filtering-on-inductive-learning\/","title":{"rendered":"Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties effect average-case learning performance. For example, we would like to quantify the value of constructive induction, noise filtering, and background knowledge. This paper works towards this goal by combining psychology&#8217;s mathematical learning theory with computational learning theory. This paper defines the effective dimension, a new learning measure that empirically links problem properties to learning performance. Like the Vapnik-Chervonenkis(VC) dimension, the effective dimension is often in a simple linear relation with problem properties. Unlike the VC dimension, the effective dimension is estimated empirically and makes average-case predictions. It is therefore more widely applicable to machine-and human-learning research. The measure is demonstrated on several learning systems including Backpropagation. Finally, the measure is used to precisely predict the benefit of using FRINGE, a feature construction system. The benefit is found to decrease as the complexity of the target concept increases.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties effect average-case learning performance. For example, we would like to quantify the value of constructive induction, noise filtering, and background knowledge. This paper works towards this goal by combining psychology&#8217;s mathematical learning theory with computational learning theory. 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