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» Learning By Observation Using Qualitative Spatial Relations
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ITCC
2005
IEEE
15 years 10 months ago
A Scalable Generative Topographic Mapping for Sparse Data Sequences
We propose a novel, computationally efficient generative topographic model for inferring low dimensional representations of high dimensional data sets, designed to exploit data s...
Ata Kabán
EMNLP
2004
15 years 5 months ago
A Boosting Algorithm for Classification of Semi-Structured Text
The focus of research in text classification has expanded from simple topic identification to more challenging tasks such as opinion/modality identification. Unfortunately, the la...
Taku Kudo, Yuji Matsumoto
SAC
2009
ACM
15 years 11 months ago
Applying latent dirichlet allocation to group discovery in large graphs
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
Keith Henderson, Tina Eliassi-Rad
BMCBI
2004
106views more  BMCBI 2004»
15 years 4 months ago
Spotting effect in microarray experiments
Background: Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects...
Tristan Mary-Huard, Jean-Jacques Daudin, Sté...
ROMAN
2007
IEEE
127views Robotics» more  ROMAN 2007»
15 years 10 months ago
Incremental on-line hierarchical clustering of whole body motion patterns
Abstract— This paper describes a novel algorithm for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are ed...
Dana Kulic, Wataru Takano, Yoshihiko Nakamura