Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Relational Data Mining
Artificial Intelligence Review - Special issue on lazy learning
Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Classifying protein fingerprints
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Distances and (Indefinite) Kernels for Sets of Objects
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Distances between Data Sets Based on Summary Statistics
The Journal of Machine Learning Research
An efficient algorithm for local distance metric learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning convex combinations of continuously parameterized basic kernels
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Scaling Up a Metric Learning Algorithm for Image Recognition and Representation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A Random Extension for Discriminative Dimensionality Reduction and Metric Learning
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Adapted transfer of distance measures for quantitative structure-activity relationships
DS'10 Proceedings of the 13th international conference on Discovery science
On improving dissimilarity-based classifications using a statistical similarity measure
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Fusing heterogeneous data sources considering a set of equivalence constraints
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Bispectral-based methods for clustering time series
Computational Statistics & Data Analysis
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The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Both the representation of the learning instances and the dissimilarity employed on that representation should be determined on the basis of domain knowledge. However, even in the presence of domain knowledge, it can be far from obvious which complex representation should be used or which dissimilarity should be applied on the chosen representation. In this paper we present a framework that allows to combine different complex representations of a given learning problem and/or different dissimilarities defined on these representations. We build on ideas developed previously on metric learning for vectorial data. We demonstrate the utility of our method in domains in which the learning instances are represented as sets of vectors by learning how to combine different set distance measures.