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» Using Learning for Approximation in Stochastic Processes
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TASLP
2008
131views more  TASLP 2008»
13 years 7 months ago
A Noise-Robust FFT-Based Auditory Spectrum With Application in Audio Classification
In this paper, we investigate the noise robustness of Wang and Shamma's early auditory (EA) model for the calculation of an auditory spectrum in audio classification applicati...
Wei Chu, B. Champagne
ICASSP
2011
IEEE
12 years 11 months ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes
IJCAI
2007
13 years 8 months ago
A Fully Connectionist Model Generator for Covered First-Order Logic Programs
We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples,...
Sebastian Bader, Pascal Hitzler, Steffen Höll...
ICML
2007
IEEE
14 years 8 months ago
Unsupervised estimation for noisy-channel models
Shannon's Noisy-Channel model, which describes how a corrupted message might be reconstructed, has been the corner stone for much work in statistical language and speech proc...
Markos Mylonakis, Khalil Sima'an, Rebecca Hwa
ICDM
2007
IEEE
131views Data Mining» more  ICDM 2007»
13 years 11 months ago
Predicting and Optimizing Classifier Utility with the Power Law
When data collection is costly and/or takes a significant amount of time, an early prediction of the classifier performance is extremely important for the design of the data minin...
Mark Last