Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

11

ECML
2006
Springer

favoriteEmaildiscussreport

78views Machine Learning» more ECML 2006»

Why Is Rule Learning Optimistic and How to Correct It

13 years 8 months ago

Why Is Rule Learning Optimistic and How to Correct It

Download www.ailab.si

Abstract. In their search through a huge space of possible hypotheses, rule induction algorithms compare estimations of qualities of a large number of rules to find the one that appears to be best. This mechanism can easily find random patterns in the data which will

Martin Mozina, Janez Demsar, Jure Zabkar, Ivan Bra

Real-time Traffic

Algorithms Compare Estimations | Data Sets | ECML 2006 | Machine Learning | Rule Evaluation Functions |

claim paper

Related Content

» Automatic Preposition Errors Correction Using Inductive Learning

» An improved machine learning protocol for the identification of correct Sequest search res...

» Learning to Identify Fragmented Words in Spoken Discourse

» Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences

» Boosting in the Limit Maximizing the Margin of Learned Ensembles

» How to distinguish posed from spontaneous smiles using geometric features

» Unsupervised Classification of Complex Clusters in Networks of Spiking Neurons

» Combining Knowledge Discovery and Knowledge Engineering to Build IDSs

» Explanatory Debugging Supporting EndUser Debugging of MachineLearned Programs

Post Info
More Details (n/a)

Added	22 Aug 2010
Updated	22 Aug 2010
Type	Conference
Year	2006
Where	ECML
Authors	Martin Mozina, Janez Demsar, Jure Zabkar, Ivan Bratko

Comments (0)