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NIPS
1998
15 years 8 months ago
Learning from Dyadic Data
Dyadic data refers to a domain with two nite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This type of data arises natur...
Thomas Hofmann, Jan Puzicha, Michael I. Jordan
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
15 years 7 months ago
Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions
Affinity functions are the core components in negative selection to discriminate self from non-self. It has been shown that affinity functions such as the r-contiguous distance an...
Thomas Stibor
BIOINFORMATICS
2006
89views more  BIOINFORMATICS 2006»
15 years 6 months ago
Maximum significance clustering of oligonucleotide microarrays
Affymetrix high-density oligonucleotide microarrays measure expression of DNA transcripts using probesets, i.e. multiple probes per transcript. Usually, these multiple measurement...
Dick de Ridder, Frank J. T. Staal, Jacques J. M. v...
PRL
2006
77views more  PRL 2006»
15 years 6 months ago
Wavelet based approach to cluster analysis. Application on low dimensional data sets
In this paper, we present a wavelet based approach which tries to automatically find the number of clusters present in a data set, along with their position and statistical proper...
Xavier Otazu, Oriol Pujol
TIT
2008
97views more  TIT 2008»
15 years 6 months ago
Adaptive Coding and Prediction of Sources With Large and Infinite Alphabets
The problem of predicting a sequence x ; x ; . . . generated by a discrete source with unknown statistics is considered. Each letter x is predicted using the information on the wor...
Boris Ryabko, Jaakko Astola, Alexander Gammerman