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» A Spectral Algorithm for Learning Hidden Markov Models
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ICIP
2002
IEEE
14 years 9 months ago
Unsupervised image segmentation via Markov trees and complex wavelets
The goal in image segmentation is to label pixels in an image based on the properties of each pixel and its surrounding region. Recently Content-Based Image Retrieval (CBIR) has e...
Cián W. Shaffrey, Ian Jermyn, Nick G. Kings...
NIPS
2000
13 years 9 months ago
Discovering Hidden Variables: A Structure-Based Approach
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As s...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koll...
CORR
2010
Springer
136views Education» more  CORR 2010»
13 years 5 months ago
The Highest Expected Reward Decoding for HMMs with Application to Recombination Detection
Abstract. Hidden Markov models are traditionally decoded by the Viterbi algorithm which finds the highest probability state path in the model. In recent years, several limitations ...
Michal Nánási, Tomás Vinar, B...
ISNN
2005
Springer
14 years 1 months ago
FPGA Realization of a Radial Basis Function Based Nonlinear Channel Equalizer
In this paper we propose a radial basis function (RBF) neural network for nonlinear time-invariant channel equalizer. The RBF network model has a three-layer structure which is com...
Poyueh Chen, Hungming Tsai, ChengJian Lin, ChiYung...
CORR
2010
Springer
146views Education» more  CORR 2010»
13 years 7 months ago
Active Learning for Hidden Attributes in Networks
In many networks, vertices have hidden attributes that are correlated with the network's topology. For instance, in social networks, people are more likely to be friends if t...
Xiaoran Yan, Yaojia Zhu, Jean-Baptiste Rouquier, C...