Sciweavers

ICML
2004
IEEE
14 years 8 months ago
Margin based feature selection - theory and algorithms
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 ...
Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
ICML
2004
IEEE
14 years 8 months ago
A MFoM learning approach to robust multiclass multi-label text categorization
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...
Sheng Gao, Wen Wu, Chin-Hui Lee, Tat-Seng Chua
ICML
2004
IEEE
14 years 8 months ago
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5
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...
Evgeniy Gabrilovich, Shaul Markovitch
ICML
2004
IEEE
14 years 8 months ago
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
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...
Glenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao
ICML
2004
IEEE
14 years 8 months ago
Relational sequential inference with reliable observations
We present a trainable sequential-inference technique for processes with large state and observation spaces and relational structure. Our method assumes "reliable observation...
Alan Fern, Robert Givan
ICML
2004
IEEE
14 years 8 months ago
Solving cluster ensemble problems by bipartite graph partitioning
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...
Xiaoli Zhang Fern, Carla E. Brodley
ICML
2004
IEEE
14 years 8 months ago
A Monte Carlo analysis of ensemble classification
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...
Roberto Esposito, Lorenza Saitta
ICML
2004
IEEE
14 years 8 months ago
Lookahead-based algorithms for anytime induction of decision trees
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, ...
Saher Esmeir, Shaul Markovitch
ICML
2004
IEEE
14 years 8 months ago
K-means clustering via principal component analysis
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...
Chris H. Q. Ding, Xiaofeng He