1282 search results - page 148 / 257 » Feature selection with neural networks |
ISNN
2004
Springer
14 years 2 months ago
2004
Springer
Abstract. Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best r...
CORR
2011
Springer
13 years 3 months ago
2011
Springer
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunki...
IJCNN
2006
IEEE
14 years 2 months ago
2006
IEEE
With very noisy data, having plentiful samples eliminates overfitting in nonlinear regression, but not in nonlinear principal component analysis (NLPCA). To overcome this problem...
IWANN
2011
Springer
12 years 11 months ago
2011
Springer
Abstract. We study how primary tactile afferents encode relevant contact features to mediate early processing of haptic information. In this paper, we apply metrical information t...
ACL
2012
11 years 11 months ago
2012
Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are b...