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ICML
2007
IEEE
14 years 8 months ago
Learning to combine distances for complex representations
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. Bo...
Adam Woznica, Alexandros Kalousis, Melanie Hilario
NN
2002
Springer
125views Neural Networks» more  NN 2002»
13 years 7 months ago
Generalized relevance learning vector quantization
We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of ...
Barbara Hammer, Thomas Villmann
WG
1993
Springer
14 years 3 days ago
Graphs, Hypergraphs and Hashing
Minimal perfect hash functions are used for memory efficient storage and fast retrieval of items from static sets. We present an infinite family of efficient and practical algori...
George Havas, Bohdan S. Majewski, Nicholas C. Worm...
KDD
2009
ACM
191views Data Mining» more  KDD 2009»
14 years 18 days ago
Improving data mining utility with projective sampling
Overall performance of the data mining process depends not just on the value of the induced knowledge but also on various costs of the process itself such as the cost of acquiring...
Mark Last
KDD
2012
ACM
205views Data Mining» more  KDD 2012»
11 years 10 months ago
Rank-loss support instance machines for MIML instance annotation
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
Forrest Briggs, Xiaoli Z. Fern, Raviv Raich