80 search results - page 10 / 16 » Learning non-stationary conditional probability distribution... |
ICDM
2009
IEEE
13 years 7 months ago
2009
IEEE
Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from mea...
KDD
2008
ACM
14 years 10 months ago
2008
ACM
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
JMLR
2006
13 years 9 months ago
2006
A classical approach in multi-class pattern classification is the following. Estimate probability distributions that generated the observations for each label class, and then labe...
UAI
2003
13 years 11 months ago
2003
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
NN
2006
Springer
13 years 9 months ago
2006
Springer
This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity thresholdbased and a local error-based insertion c...