ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
In this work we consider the task of relaxing the i.i.d assumption in online pattern recognition (or classification), aiming to make existing learning algorithms applicable to a ...
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
While there is a lot of empirical evidence showing that traditional rule learning approaches work well in practice, it is nearly impossible to derive analytical results about thei...
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary,...
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
This paper describes an e cient method for learning the parameters of a Gaussian process (GP). The parameters are learned from multiple tasks which are assumed to have been drawn ...
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optim...
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...
ICML
2004
IEEE
14 years 4 months ago
2004
IEEE
This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC stat...