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» Learning Expressive Models for Word Sense Disambiguation
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JMLR
2008
209views more  JMLR 2008»
13 years 7 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
ICDM
2010
IEEE
197views Data Mining» more  ICDM 2010»
13 years 5 months ago
D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification
: D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi HP Labo...
Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yu...
CEC
2005
IEEE
14 years 1 months ago
Investigating the effect of random noise on the evolution of colour terms
Abstract- The effect of adding noise to an expressioninduction model of language evolution was investigated. The model consisted of a number of artificial people who were able to i...
Mike Dowman
ESWS
2010
Springer
13 years 6 months ago
The Semantic Gap of Formalized Meaning
Recent work in Ontology learning and Text mining has mainly focused on engineering methods to solve practical problem. In this thesis, we investigate methods that can substantially...
Sebastian Hellmann
SIAMIS
2011
13 years 2 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch