Prototype selection for dissimilarity-based classifiers
By Elzbieta Pekalska, Robert P. W. Duin, Pavel Paclík. Pattern Recognition, 2006
344 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/64f7e1cc695de39ee145e90b8cd959a65eac4739
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Eamonn J. Keogh
- Horst Bunke
- Kaspar Riesen
- Riccardo Satta
- Elena Marchiori
- Michel Neuhaus
- Chotirat (Ann) Ratanamahatana
- Elzbieta Pekalska
In Computer Vision and Pattern Recognition
- Kaspar Riesen
- Horst Bunke
- Elena Marchiori
- Elzbieta Pekalska
- Michel Neuhaus
- Chotirat (Ann) Ratanamahatana
- George Economou
- Filiberto Pla
Citations in the corpus
- Class Conditional Nearest Neighbor and Large Margin Instance Selection (2008, methodology): “In order to tackle these problems, various algorithms have been developed, such as those for instance/prototype selection [1], [2], [3], [8], [19], [22], [31], for feature selection [20], [25], [36], and for distance learning [29], [39], [40].”
- Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces (2006, methodology): “Here, we show that the prototype sets, optimised to guarantee good performance of the 1-NN rule, lead to a higher classification accuracy, when more general dissimilarity-based classifiers are considered, as recently proposed [16,17].”
- Fast time series classification using numerosity reduction (2006, methodology): “Existing data reduction techniques include random reduction and data condensing/editing (Dasarathy, 1991; Pekalska et al., 2006 ).”
- Pose-based human action recognition via sparse representation in dissimilarity space (2014, background): “The properties of such spaces have not been extensively studied yet, although there is a solid justification for the construction of classifiers in dissimilarity spaces in [23].”
- Graph Embedding in Vector Spaces by Means of Prototype Selection (2007, methodology): “The idea underlying our method was originally developed for the problem of embedding sets of feature vectors in a dissimilarity space [2,3,9,10].”
- Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, background): “Thus, some prototype selection scheme [103] should be put in place to properly choose the elements of P .”