10 search results - page 2 / 2 » Deep Bottleneck Classifiers in Supervised Dimension Reductio... |
SAC
2006
ACM
13 years 11 months ago
2006
ACM
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
CVPR
2010
IEEE
14 years 1 months ago
2010
IEEE
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensional...
IRI
2007
IEEE
13 years 11 months ago
2007
IEEE
Many applications require analyzing vast amounts of textual data, but the size and inherent noise of such data can make processing very challenging. One approach to these issues i...
BMCBI
2010
13 years 5 months ago
2010
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
JMLR
2008
13 years 5 months ago
2008
In supervised learning, a training set consisting of labeled instances is used by a learning algorithm for generating a model (classifier) that is subsequently employed for decidi...