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» Active Inference in Concept Learning
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KDD
2009
ACM
205views Data Mining» more  KDD 2009»
14 years 2 months ago
From active towards InterActive learning: using consideration information to improve labeling correctness
Data mining techniques have become central to many applications. Most of those applications rely on so called supervised learning algorithms, which learn from given examples in th...
Abraham Bernstein, Jiwen Li
ICMCS
2005
IEEE
105views Multimedia» more  ICMCS 2005»
14 years 1 months ago
Speech-Based Visual Concept Learning Using Wordnet
Modeling visual concepts using supervised or unsupervised machine learning approaches are becoming increasing important for video semantic indexing, retrieval, and filtering appli...
Xiaodan Song, Ching-Yung Lin, Ming-Ting Sun
ICGI
2010
Springer
13 years 5 months ago
Learning Context Free Grammars with the Syntactic Concept Lattice
The Syntactic Concept Lattice is a residuated lattice based on the distributional structure of a language; the natural representation based on this is a context sensitive formalism...
Alexander Clark
MLDM
2001
Springer
14 years 2 days ago
Concepts Learning with Fuzzy Clustering and Relevance Feedback
Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications . . . . . . . . . . . . . . . . . 23 R. Schmidt and L. Gierl Are Case-Based Reasoning and...
Bir Bhanu, Anlei Dong
FOIKS
2008
Springer
13 years 9 months ago
Cost-Minimising Strategies for Data Labelling: Optimal Stopping and Active Learning
Supervised learning deals with the inference of a distribution over an output or label space Y conditioned on points in an observation space X , given a training dataset D of pair...
Christos Dimitrakakis, Christian Savu-Krohn