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» A Unifying Model of Variables and Names
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ICML
2010
IEEE
14 years 10 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre
BMCBI
2008
153views more  BMCBI 2008»
14 years 9 months ago
How to make the most of NE dictionaries in statistical NER
Background: When term ambiguity and variability are very high, dictionary-based Named Entity Recognition (NER) is not an ideal solution even though large-scale terminological reso...
Yutaka Sasaki, Yoshimasa Tsuruoka, John McNaught, ...
NIPS
2004
14 years 11 months ago
Conditional Random Fields for Object Recognition
We present a discriminative part-based approach for the recognition of object classes from unsegmented cluttered scenes. Objects are modeled as flexible constellations of parts co...
Ariadna Quattoni, Michael Collins, Trevor Darrell
ICDM
2010
IEEE
200views Data Mining» more  ICDM 2010»
14 years 6 months ago
Bayesian Maximum Margin Clustering
Abstract--Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered...
Bo Dai, Baogang Hu, Gang Niu
140
Voted
ER
1995
Springer
152views Database» more  ER 1995»
15 years 1 months ago
A Logic Framework for a Semantics of Object-Oriented Data Modeling
We describe a (meta) formalism for defining a variety of (object oriented) data models in a unified framework based on a variation of first-order logic. As specific example we use...
Olga De Troyer, Robert Meersman