6 search results - page 1 / 2 » Limitations of Learning via Embeddings in Euclidean Half-Spa... |
COLT
2001
Springer
13 years 9 months ago
2001
Springer
The notion of embedding a class of dichotomies in a class of linear half spaces is central to the support vector machines paradigm. We examine the question of determining the mini...
CVIU
2011
12 years 8 months ago
2011
In this paper, we tackle the problem of embedding a set of relational structures into a metric space for purposes of matching and categorisation. To this end, we view the problem ...
IJCAI
2007
13 years 6 months ago
2007
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
ICML
2002
IEEE
14 years 5 months ago
2002
IEEE
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is perfor...
PKDD
2009
Springer
13 years 11 months ago
2009
Springer
Most existing semi-supervised learning methods are based on the smoothness assumption that data points in the same high density region should have the same label. This assumption, ...