1215 search results - page 10 / 243 » Dimensions of machine learning in design |
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensiona...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and pra...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Intuitively, learning should be easier when the data points lie on a low-dimensional submanifold of the input space. Recently there has been a growing interest in algorithms that ...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...
COLT
1991
Springer
13 years 11 months ago
1991
Springer
While most theoretical work in machine learning has focused on the complexity of learning, recently there has been increasing interest in formally studying the complexity of teach...