Natural Language Processing and Machine Learning

By T. Schindler, D. Wolter, Ute Schmid. 2016

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Citations in the corpus

Auditing black-box models for indirect influence (2016, background): “By a “model of a model” we mean that we should 1) train the model f on training data ( X, Y ) , 2) determine new labels ˆ Y from the predicted outcomes f ( X ) , and 3) overﬁt an interpretable model I ( f ) to these predicted labels (as done in [3]).”
Comprehensible credit scoring models using rule extraction from support vector machines (2007, neutral): “This approach has been used in [4] to extract rules from SVMs.”
Binarized Support Vector Machines (2010, unknown): “Binarizing continuous predictor variables has also been proposed in the so-called rule extraction procedures within SVM (Barakat and Diederich 2004, 2005; Fung et al. 2005; Martens et al. 2007; Núñez et al. 2002) and neural networks (Andrews et al. 1995, Baesens et al. 2003, Craven and Shavlik…”
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (2008, unknown): “This approach has been used in [5] to extract rules from SVMs.”
Hybrid support vector machine rule extraction method for discovering the preferences of stock market investors: Evidence from Montenegro (2015, methodology): “According to Barakat and Diederich (2004), rule extraction techniques can be divided into two categories, i.e., “transparent” and “pedagogical,” depending on whether they rely on the internal structure of the SVM.”
Auditing Black-box Models by Obscuring Features (2016, background): “By a “model of a model” we mean that we should 1) train the model M on training data (X, Y ), 2) determine new labels Ŷ from the predicted outcomes M(X), and 3) overfit an interpretable model I(M) to these predicted labels (see Algorithm 3 and [1]).”
Eclectic Rule-Extraction from Support Vector Machines (2005, unknown): “Having identified the importance of rule-extraction algorithms, an overview of the problem to be addressed is given here.”