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» Approximate Kernel Clustering
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DAGM
2011
Springer
13 years 9 months ago
Relaxed Exponential Kernels for Unsupervised Learning
Many unsupervised learning algorithms make use of kernels that rely on the Euclidean distance between two samples. However, the Euclidean distance is optimal for Gaussian distribut...
Karim T. Abou-Moustafa, Mohak Shah, Fernando De la...
73
Voted
ICML
2006
IEEE
15 years 10 months ago
Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution
The Dirichlet compound multinomial (DCM) distribution, also called the multivariate Polya distribution, is a model for text documents that takes into account burstiness: the fact ...
Charles Elkan
RTCSA
2003
IEEE
15 years 3 months ago
An Approximation Algorithm for Broadcast Scheduling in Heterogeneous Clusters
Network of workstation (NOW) is a cost-effective alternative to massively parallel supercomputers. As commercially available off-theshelf processors become cheaper and faster, it...
Pangfeng Liu, Da-Wei Wang, Yi-Heng Guo
COMPGEOM
2011
ACM
14 years 1 months ago
Comparing distributions and shapes using the kernel distance
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phil...
ISAAC
2004
Springer
121views Algorithms» more  ISAAC 2004»
15 years 3 months ago
Approximate Distance Oracles for Graphs with Dense Clusters
Let G be a graph containing N disjoint t-spanners that are inter-connected with M edges. We present an algorithm that constructs a data structure of size O(M2 + n log n) that answ...
Mattias Andersson, Joachim Gudmundsson, Christos L...