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GECCO
2007
Springer
212views Optimization» more  GECCO 2007»
16 years 24 days ago
A developmental model of neural computation using cartesian genetic programming
The brain has long been seen as a powerful analogy from which novel computational techniques could be devised. However, most artificial neural network approaches have ignored the...
Gul Muhammad Khan, Julian F. Miller, David M. Hall...
DEXAW
2010
IEEE
196views Database» more  DEXAW 2010»
15 years 6 months ago
Direct Optimization of Evaluation Measures in Learning to Rank Using Particle Swarm
— One of the central issues in Learning to Rank (L2R) for Information Retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures ...
Ósscar Alejo, Juan M. Fernández-Luna...
ICML
2004
IEEE
16 years 7 months ago
Learning random walk models for inducing word dependency distributions
Many NLP tasks rely on accurately estimating word dependency probabilities P(w1|w2), where the words w1 and w2 have a particular relationship (such as verb-object). Because of the...
Kristina Toutanova, Christopher D. Manning, Andrew...
IJON
2008
186views more  IJON 2008»
15 years 6 months ago
Computational analysis and learning for a biologically motivated model of boundary detection
In this work we address the problem of boundary detection by combining ideas and approaches from biological and computational vision. Initially, we propose a simple and efficient ...
Iasonas Kokkinos, Rachid Deriche, Olivier D. Fauge...
IJCNN
2008
IEEE
16 years 1 months ago
Learning adaptive subject-independent P300 models for EEG-based brain-computer interfaces
Abstract— This paper proposes an approach to learn subjectindependent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of exis...
Shijian Lu, Cuntai Guan, Haihong Zhang