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» MILIS: Multiple Instance Learning with Instance Selection
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COLING
2002
13 years 7 months ago
Fine Grained Classification of Named Entities
While Named Entity extraction is useful in many natural language applications, the coarse categories that most NE extractors work with prove insufficient for complex applications ...
Michael Fleischman, Eduard H. Hovy
JMLR
2010
136views more  JMLR 2010»
13 years 2 months ago
Reducing Label Complexity by Learning From Bags
We consider a supervised learning setting in which the main cost of learning is the number of training labels and one can obtain a single label for a bag of examples, indicating o...
Sivan Sabato, Nathan Srebro, Naftali Tishby
COLT
2008
Springer
13 years 8 months ago
Teaching Dimensions based on Cooperative Learning
The problem of how a teacher and a learner can cooperate in the process of learning concepts from examples in order to minimize the required sample size without “coding tricksâ€...
Sandra Zilles, Steffen Lange, Robert Holte, Martin...
ECIR
2009
Springer
14 years 4 months ago
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve
Abstract. Learning ranking functions is crucial for solving many problems, ranging from document retrieval to building recommendation systems based on an individual user’s prefer...
Pinar Donmez, Jaime G. Carbonell
AUSAI
2008
Springer
13 years 9 months ago
Learning a Generative Model for Structural Representations
Abstract. Graph-based representations have been used with considercess in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the ...
Andrea Torsello, David L. Dowe