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» On learning algorithm selection for classification
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KDD
2004
ACM
150views Data Mining» more  KDD 2004»
14 years 9 months ago
Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts
Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine online opinions from ...
Edoardo Airoldi, Xue Bai, Rema Padman
FGR
2004
IEEE
161views Biometrics» more  FGR 2004»
14 years 20 days ago
AdaBoost with Totally Corrective Updates for Fast Face Detection
An extension of the AdaBoost learning algorithm is proposed and brought to bear on the face detection problem. In each weak classifier selection cycle, the novel totally correctiv...
Jan Sochman, Jiri Matas
ECML
2006
Springer
14 years 18 days ago
An Adaptive Kernel Method for Semi-supervised Clustering
Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introd...
Bojun Yan, Carlotta Domeniconi
ICML
2003
IEEE
14 years 9 months ago
Planning in the Presence of Cost Functions Controlled by an Adversary
We investigate methods for planning in a Markov Decision Process where the cost function is chosen by an adversary after we fix our policy. As a running example, we consider a rob...
H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum
ICDM
2009
IEEE
137views Data Mining» more  ICDM 2009»
14 years 3 months ago
Set-Based Boosting for Instance-Level Transfer
—The success of transfer to improve learning on a target task is highly dependent on the selected source data. Instance-based transfer methods reuse data from the source tasks to...
Eric Eaton, Marie desJardins