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ICML
2005
IEEE
14 years 9 months ago
Expectation maximization algorithms for conditional likelihoods
We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the dist...
Jarkko Salojärvi, Kai Puolamäki, Samuel ...
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
14 years 2 months ago
Nonlinear feature extraction using a neuro genetic hybrid
Feature extraction is a process that extracts salient features from observed variables. It is considered a promising alternative to overcome the problems of weight and structure o...
Yung-Keun Kwon, Byung Ro Moon
AUSAI
2006
Springer
14 years 14 days ago
Robust Character Recognition Using a Hierarchical Bayesian Network
There is increasing evidence to suggest that the neocortex of the mammalian brain does not consist of a collection of specialised and dedicated cortical architectures, but instead ...
John Thornton, Torbjorn Gustafsson, Michael Blumen...
ICASSP
2010
IEEE
13 years 3 months ago
Unsupervised knowledge acquisition for Extracting Named Entities from speech
This paper presents a Named Entity Recognition (NER) method dedicated to process speech transcriptions. The main principle behind this method is to collect in an unsupervised way ...
Frédéric Béchet, Eric Charton
ICDM
2007
IEEE
174views Data Mining» more  ICDM 2007»
14 years 3 months ago
Targeting Input Data for Acoustic Bird Species Recognition Using Data Mining and HMMs
In this paper we propose the integration of Data Mining with Hidden Markov Models when applied to the problem of acoustic bird species recognition. We first show how each of them...
Erika Vilches, Ivan A. Escobar, Edgar E. Vallejo, ...