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» Learning from Highly Structured Data by Decomposition
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ICANN
2009
Springer
15 years 9 months ago
Learning Complex Population-Coded Sequences
The sequential structure of complex actions is apparently at an abstract “cognitive” level in several regions of the frontal cortex, independent of the control of the immediate...
Kiran V. Byadarhaly, Mithun Perdoor, Suresh Vasa, ...
EVOW
2007
Springer
15 years 8 months ago
Classification of Cell Fates with Support Vector Machine Learning
In human mesenchymal stem cells the envelope surrounding the nucleus, as visualized by the nuclear lamina, has a round and flat shape. The lamina structure is considerably deformed...
Ofer M. Shir, Vered Raz, Roeland W. Dirks, Thomas ...
WEBI
2010
Springer
15 years 2 months ago
DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...
Su Yan, Sofien Bouaziz, Dongwon Lee
AAAI
2006
15 years 5 months ago
Semi-supervised Multi-label Learning by Constrained Non-negative Matrix Factorization
We present a novel framework for multi-label learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key a...
Yi Liu, Rong Jin, Liu Yang
ICASSP
2011
IEEE
14 years 8 months ago
Exploiting active-learning strategies for annotating prosodic events with limited labeled data
Many applications of spoken-language systems can benefit from having access to annotations of prosodic events. Unfortunately, obtaining human annotations of these events, even se...
Raul Fernandez, Bhuvana Ramabhadran