Construction of class regions by a randomized algorithm: a randomized subclass method
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Cited by (26)
An efficient construction and application usefulness of rectangle greedy covers
2014, Pattern RecognitionCitation Excerpt :They proposed a method that uses all the MEPSs [4], but the proposed MEPS enumeration algorithm was too slow to use practically. Later, they proposed the randomized subclass method (RSM) [2], which uses k randomly generated MEPSs for each class, where k is an input parameter. An RGC needs more time to construct than a set of rectangles constructed by RSM but uses a smaller number of rectangles in most cases without degrading classification performance so much.
Convex sets as prototypes for classifying patterns
2009, Engineering Applications of Artificial IntelligenceCitation Excerpt :We presented the preliminary version of this work in Takigawa et al. (2005), focusing on minimum enclosing balls as convex sets. The idea was originated in the special case of minimum enclosing axis-parallel boxes (Kudo et al., 1996; Takigawa et al., 2004). It is also related with logical analysis of data (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 quantization (Hastie et al., 2001), and classifiers based on explicit computational geometric structure such as support vector machine (Schölkopf and Smola, 2001).
Non-parametric classifier-independent feature selection
2006, Pattern RecognitionMultidimensional curve classification using passing-through regions
1999, Pattern Recognition LettersA global learning algorithm for a RBF network
1999, Neural NetworksApproximation of class regions by quasi convex hulls
1998, Pattern Recognition Letters