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» Extracting Approximate Patterns
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PAMI
2006
127views more  PAMI 2006»
15 years 6 months ago
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
Understanding the structure of multidimensional patterns, especially in unsupervised case, is of fundamental importance in data mining, pattern recognition and machine learning. Se...
Martin H. C. Law, Anil K. Jain
IJON
2007
109views more  IJON 2007»
15 years 6 months ago
Monophonic sound source separation with an unsupervised network of spiking neurones
We incorporate auditory-based features into an unconventional pattern classification system, consisting of a network of spiking neurones with dynamical and multiplicative synapse...
Ramin Pichevar, Jean Rouat
MCS
2002
Springer
15 years 6 months ago
Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach
The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid intelligent methods for mining the data. This focus is required even mo...
Emilio Benfenati, Paolo Mazzatorta, Daniel Neagu, ...
SPEECH
2002
113views more  SPEECH 2002»
15 years 6 months ago
Estimation of the signal-to-noise ratio with amplitude modulation spectrograms
An algorithm is proposed which automatically estimates the local signalto-noise ratio (SNR) between speech and noise. The feature extraction stage of the algorithm is motivated by...
Jürgen Tchorz, Birger Kollmeier
VLSISP
2002
139views more  VLSISP 2002»
15 years 6 months ago
A Modified Minimum Classification Error (MCE) Training Algorithm for Dimensionality Reduction
Dimensionality reduction is an important problem in pattern recognition. There is a tendency of using more and more features to improve the performance of classifiers. However, not...
Xuechuan Wang, Kuldip K. Paliwal