ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
Feature selection is the task of choosing a small set out of a given set of features that capture the relevant properties of the data. In the context of supervised classification ...
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
We propose a multiclass (MC) classification approach to text categorization (TC). To fully take advantage of both positive and negative training examples, a maximal figure-of-meri...
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge numbers of features. Most previous studies found that the major...
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the us...
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
We present a trainable sequential-inference technique for processes with large state and observation spaces and relational structure. Our method assumes "reliable observation...
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
A critical problem in cluster ensemble research is how to combine multiple clusterings to yield a final superior clustering result. Leveraging advanced graph partitioning techniqu...
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
In this paper we extend previous results providing a theoretical analysis of a new Monte Carlo ensemble classifier. The framework allows us to characterize the conditions under wh...
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
ICML
2004
IEEE
15 years 15 days ago
2004
IEEE
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...