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AAAI
2007
13 years 9 months ago
Multi-Label Learning by Instance Differentiation
Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is...
Min-Ling Zhang, Zhi-Hua Zhou
TSMC
2010
13 years 2 months ago
Interaction Modeling and Prediction in Smart Spaces: A Bio-Inspired Approach Based on Autobiographical Memory
In Smart Spaces (SSs), the capability of learning from experience is fundamental for autonomous adaptation to environmental changes and for proactive interaction with users. New re...
Alessio Dore, Andrea F. Cattoni, Carlo S. Regazzon...
AMEC
2003
Springer
14 years 17 days ago
Improving Learning Performance by Applying Economic Knowledge
Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this spac...
Christopher H. Brooks, Robert S. Gazzale, Jeffrey ...
FOCS
2010
IEEE
13 years 5 months ago
Learning Convex Concepts from Gaussian Distributions with PCA
We present a new algorithm for learning a convex set in n-dimensional space given labeled examples drawn from any Gaussian distribution. The complexity of the algorithm is bounded ...
Santosh Vempala
GOSLER
1995
13 years 11 months ago
Learning and Consistency
In designing learning algorithms it seems quite reasonable to construct them in such a way that all data the algorithm already has obtained are correctly and completely reflected...
Rolf Wiehagen, Thomas Zeugmann