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» Learning the Structure of Linear Latent Variable Models
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NECO
1998
119views more  NECO 1998»
13 years 7 months ago
Density Estimation by Mixture Models with Smoothing Priors
In the statistical approach for self-organizing maps (SOMs), learning is regarded as an estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior on the ce...
Akio Utsugi
AAAI
1992
13 years 8 months ago
Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning
It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exp...
Thomas Dean, Dana Angluin, Kenneth Basye, Sean P. ...
CORR
2008
Springer
143views Education» more  CORR 2008»
13 years 7 months ago
Join Bayes Nets: A new type of Bayes net for relational data
Many real-world data are maintained in relational format, with different tables storing information about entities and their links or relationships. The structure (schema) of the ...
Oliver Schulte, Hassan Khosravi, Flavia Moser, Mar...
CSUR
1999
159views more  CSUR 1999»
13 years 7 months ago
Hubs, authorities, and communities
The Web can be naturally modeled as a directed graph, consisting of a set of abstract nodes (the pages) joined by directional edges (the hyperlinks). Hyperlinks encode a considerab...
Jon M. Kleinberg
AAAI
2008
13 years 10 months ago
Sparse Projections over Graph
Recent study has shown that canonical algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be obtained from graph based dimensionality ...
Deng Cai, Xiaofei He, Jiawei Han