Classification Using Class Cover Catch Digraphs

By Carey E. Priebe, David J. Marchette, Jason G. DeVinney, Diego A. Socolinsky. 2003

79 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

This looks like that: deep learning for interpretable image recognition (2018, methodology): “Our work relates closely to other prototype classification techniques in machine learning [32, 2, 45], and very closely, to the Bayesian Case Model [18].”
A new family of random graphs for testing spatial segregation (2007, background): “DeVinney et al. (2002), Marchette and Priebe (2003), Priebe et al. (2003b), and Priebe et al. (2003a) demonstrated relatively good performance of it in classification.”
Convex sets as prototypes for classifying patterns (2009, methodology): “…(LAD) with boxes (Alexe and Hammer, 2006; Eckstein et al., 2002), ball-based combinatorial classifier (Cannon et al., 2002; Cannon and Cowen, 2004; Priebe et al., 2003; Marchette, 2004; DeVinney, 2003), conventional prototype-based methods such as nearest neighbors and learning vector…”
A SLLN for a one-dimensional class cover problem (2002, methodology): “More elaborate methods of applying the CCP to classiﬁcation are presented in [PMDS02].”
Examples are not enough, learn to criticize! Criticism for Interpretability (2016, background): “There is also a large literature on variations of the set cover problem tuned for classification, such as the cover digraph approach of (Priebe et al., 2003) and prototype selection for interpretable classification (Bien and Tibshirani, 2011), which involves selecting prototypes that maximize the co”
This Looks Like That: Deep Learning for Interpretable Image Recognition (2018, unknown): “Our work also relates closely to other prototype classification techniques in machine learning [25, 3, 14, 5].”