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» Evaluating learning algorithms and classifiers
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KDD
2004
ACM
124views Data Mining» more  KDD 2004»
14 years 2 months ago
Incorporating prior knowledge with weighted margin support vector machines
Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus ...
Xiaoyun Wu, Rohini K. Srihari
WWW
2009
ACM
14 years 9 months ago
Unsupervised query categorization using automatically-built concept graphs
Automatic categorization of user queries is an important component of general purpose (Web) search engines, particularly for triggering rich, query-specific content and sponsored ...
Eustache Diemert, Gilles Vandelle
WSDM
2010
ACM
242views Data Mining» more  WSDM 2010»
14 years 6 months ago
Improving Ad Relevance in Sponsored Search
We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model t...
Dustin Hillard, Stefan Schroedl, Eren Manavoglu, H...
CLIN
2001
13 years 10 months ago
Multi-feature Error Detection in Spoken Dialogue Systems
The present paper evaluates the role selected features and feature combinations play for error detection in spoken dialogue systems. We investigate the relevance of various, readi...
Piroska Lendvai, Antal van den Bosch, Emiel Krahme...
ICML
2010
IEEE
13 years 10 months ago
Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes
Most existing algorithms for clinical risk stratification rely on labeled training data. Collecting this data is challenging for clinical conditions where only a small percentage ...
Zeeshan Syed, Ilan Rubinfeld