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» Learning the Ideal Evaluation Function
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JMLR
2010
121views more  JMLR 2010»
14 years 4 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
ECML
2006
Springer
15 years 1 months ago
Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data
Multiple-instance learning (MIL) is a popular concept among the AI community to support supervised learning applications in situations where only incomplete knowledge is available....
Corneliu Henegar, Karine Clément, Jean-Dani...
SIGIR
2011
ACM
14 years 13 days ago
Pseudo test collections for learning web search ranking functions
Test collections are the primary drivers of progress in information retrieval. They provide a yardstick for assessing the effectiveness of ranking functions in an automatic, rapi...
Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy L...
76
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DAGM
2007
Springer
15 years 3 months ago
Learning Robust Objective Functions with Application to Face Model Fitting
Abstract. Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge is to dete...
Matthias Wimmer, Sylvia Pietzsch, Freek Stulp, Ber...
IJON
2010
148views more  IJON 2010»
14 years 6 months ago
Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machin
Several solutions have been proposed to exploit the availability of heterogeneous sources of biomolecular data for gene function prediction, but few attention has been dedicated t...
Matteo Re, Giorgio Valentini