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» A maximum entropy approach to species distribution modeling
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IDEAL
2000
Springer
13 years 11 months ago
Quantization of Continuous Input Variables for Binary Classification
Quantization of continuous variables is important in data analysis, especially for some model classes such as Bayesian networks and decision trees, which use discrete variables. Of...
Michal Skubacz, Jaakko Hollmén
ICBA
2004
Springer
197views Biometrics» more  ICBA 2004»
14 years 1 months ago
Maximum Likelihood and Maximum a Posteriori Adaptation for Distributed Speaker Recognition Systems
We apply the ETSI’s DSR standard to speaker verification over telephone networks and investigate the effect of extracting spectral features from different stages of the ETSI...
Chin-Hung Sit, Man-Wai Mak, Sun-Yuan Kung
BMCBI
2006
118views more  BMCBI 2006»
13 years 8 months ago
Automatic generation of gene finders for eukaryotic species
Background: The number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. ...
Kasper Munch, Anders Krogh
IJCNLP
2005
Springer
14 years 1 months ago
A Chunking Strategy Towards Unknown Word Detection in Chinese Word Segmentation
This paper proposes a chunking strategy to detect unknown words in Chinese word segmentation. First, a raw sentence is pre-segmented into a sequence of word atoms 1 using a maximum...
Guodong Zhou
ICDM
2007
IEEE
174views Data Mining» more  ICDM 2007»
14 years 2 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, ...