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» Covariance Kernels from Bayesian Generative Models
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AAAI
2010
13 years 9 months ago
Adaptive Transfer Learning
Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the ...
Bin Cao, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung...
NIPS
2007
13 years 9 months ago
Receptive Fields without Spike-Triggering
Stimulus selectivity of sensory neurons is often characterized by estimating their receptive field properties such as orientation selectivity. Receptive fields are usually deriv...
Jakob Macke, Guenther Zeck, Matthias Bethge
ISBRA
2009
Springer
14 years 2 months ago
Using Gene Expression Modeling to Determine Biological Relevance of Putative Regulatory Networks
Identifying gene regulatory networks from high-throughput gene expression data is one of the most important goals of bioinformatics, but it remains difficult to define what makes a...
Peter Larsen, Yang Dai
NIPS
2001
13 years 9 months ago
On the Generalization Ability of On-Line Learning Algorithms
In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of hypotheses generated by an arbitrary on-line learning algorithm run on an independent an...
Nicolò Cesa-Bianchi, Alex Conconi, Claudio ...
MIR
2006
ACM
141views Multimedia» more  MIR 2006»
14 years 1 months ago
Mining temporal patterns of movement for video content classification
Scalable approaches to video content classification are limited by an inability to automatically generate representations of events ode abstract temporal structure. This paper pre...
Michael Fleischman, Philip DeCamp, Deb Roy