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» A Framework for Multiple-Instance Learning
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CORR
2004
Springer
140views Education» more  CORR 2004»
15 years 4 months ago
Integrating Defeasible Argumentation and Machine Learning Techniques
The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML app...
Sergio Alejandro Gómez, Carlos Iván ...
JAIR
2010
111views more  JAIR 2010»
15 years 2 months ago
Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
We present a novel framework for learning to interpret and generate language using only perceptual context as supervision. We demonstrate its capabilities by developing a system t...
David L. Chen, Joohyun Kim, Raymond J. Mooney
MICCAI
2010
Springer
15 years 2 months ago
Agreement-Based Semi-supervised Learning for Skull Stripping
Abstract. Learning-based approaches have become increasingly practical in medical imaging. For a supervised learning strategy, the quality of the trained algorithm (usually a class...
Juan Eugenio Iglesias, Cheng-Yi Liu, Paul M. Thomp...
PKDD
2010
Springer
184views Data Mining» more  PKDD 2010»
15 years 2 months ago
Shift-Invariant Grouped Multi-task Learning for Gaussian Processes
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
Yuyang Wang, Roni Khardon, Pavlos Protopapas
AAAI
2010
15 years 2 months ago
Non-I.I.D. Multi-Instance Dimensionality Reduction by Learning a Maximum Bag Margin Subspace
Multi-instance learning, as other machine learning tasks, also suffers from the curse of dimensionality. Although dimensionality reduction methods have been investigated for many ...
Wei Ping, Ye Xu, Kexin Ren, Chi-Hung Chi, Shen Fur...