Sciweavers

ECML
2001
Springer
14 years 1 days ago
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
Abstract. This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, ...
Peter D. Turney
ECML
2001
Springer
14 years 1 days ago
Symbolic Discriminant Analysis for Mining Gene Expression Patterns
Jason H. Moore, Joel S. Parker, Lance W. Hahn
ECML
2001
Springer
14 years 1 days ago
Comparing the Bayes and Typicalness Frameworks
When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these...
Thomas Melluish, Craig Saunders, Ilia Nouretdinov,...
ECML
2001
Springer
14 years 1 days ago
Learning of Variability for Invariant Statistical Pattern Recognition
In many applications, modelling techniques are necessary which take into account the inherent variability of given data. In this paper, we present an approach to model class speci...
Daniel Keysers, Wolfgang Macherey, Jörg Dahme...
ECML
2001
Springer
14 years 1 days ago
Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences
Solomonoff’s uncomputable universal prediction scheme ξ allows to predict the next
Marcus Hutter
ECML
2001
Springer
14 years 1 days ago
Iterative Double Clustering for Unsupervised and Semi-supervised Learning
We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slon...
Ran El-Yaniv, Oren Souroujon
ECML
2001
Springer
14 years 1 days ago
Wrapping Web Information Providers by Transducer Induction
Modern agent and mediator systems communicate to a multitude of Web information providers to better satisfy user requests. They use wrappers to extract relevant information from HT...
Boris Chidlovskii
ECML
2001
Springer
14 years 1 days ago
A Framework for Learning Rules from Multiple Instance Data
Abstract. This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. In a multiple-instance representation, each learning example is r...
Yann Chevaleyre, Jean-Daniel Zucker