ICML
2004
IEEE
13 years 10 months ago
2004
IEEE
ICML
2004
IEEE
13 years 10 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
13 years 10 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
13 years 10 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
13 years 10 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
13 years 10 months ago
2004
IEEE
ICML
2004
IEEE
13 years 10 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
13 years 10 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
13 years 10 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
13 years 10 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...