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APIN
1999
107views more  APIN 1999»
13 years 7 months ago
Massively Parallel Probabilistic Reasoning with Boltzmann Machines
We present a method for mapping a given Bayesian network to a Boltzmann machine architecture, in the sense that the the updating process of the resulting Boltzmann machine model pr...
Petri Myllymäki
APIN
2007
118views more  APIN 2007»
13 years 7 months ago
Using genetic algorithms to reorganize superpeer structure in peer to peer networks
In this thesis, we describe a genetic algorithm for optimizing the superpeer structure of semantic peer to peer networks. Peer to peer, also called P2P, networks enable us to sear...
Jaymin Kessler, Khaled Rasheed, Ismailcem Budak Ar...
SIGMOD
2009
ACM
175views Database» more  SIGMOD 2009»
14 years 7 months ago
Keyword search on structured and semi-structured data
Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex a...
Yi Chen, Wei Wang 0011, Ziyang Liu, Xuemin Lin
FUIN
2008
108views more  FUIN 2008»
13 years 6 months ago
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such rel...
Wannes Meert, Jan Struyf, Hendrik Blockeel
ICCV
2009
IEEE
15 years 10 days ago
Constrained Clustering by Spectral Kernel Learning
Clustering performance can often be greatly improved by leveraging side information. In this paper, we consider constrained clustering with pairwise constraints, which specify s...
Zhenguo Li, Jianzhuang Liu