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» Learning with Constrained and Unlabelled Data
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NIPS
2004
13 years 11 months ago
Sampling Methods for Unsupervised Learning
We present an algorithm to overcome the local maxima problem in estimating the parameters of mixture models. It combines existing approaches from both EM and a robust fitting algo...
Robert Fergus, Andrew Zisserman, Pietro Perona
BMCBI
2010
113views more  BMCBI 2010»
13 years 10 months ago
Unifying generative and discriminative learning principles
Background: The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of differe...
Jens Keilwagen, Jan Grau, Stefan Posch, Marc Stric...
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
14 years 4 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
KDD
2010
ACM
252views Data Mining» more  KDD 2010»
14 years 1 months ago
Fast query execution for retrieval models based on path-constrained random walks
Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph, with typed nodes representing documents, terms, and metadata, and labe...
Ni Lao, William W. Cohen
JMLR
2006
131views more  JMLR 2006»
13 years 9 months ago
On Representing and Generating Kernels by Fuzzy Equivalence Relations
Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of l...
Bernhard Moser