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» Arguing from Experience to Classifying Noisy Data
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CEC
2009
IEEE
14 years 7 days ago
Using genetic programming to obtain implicit diversity
—When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles shoul...
Ulf Johansson, Cecilia Sönströd, Tuve L&...
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 9 months ago
Detecting outliers using transduction and statistical testing
Outlier detection can uncover malicious behavior in fields like intrusion detection and fraud analysis. Although there has been a significant amount of work in outlier detection, ...
Daniel Barbará, Carlotta Domeniconi, James ...
IJCAI
2001
13 years 10 months ago
Probabilistic Classification and Clustering in Relational Data
Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best des...
Benjamin Taskar, Eran Segal, Daphne Koller
ICML
2010
IEEE
13 years 10 months ago
Active Learning for Networked Data
We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the label...
Mustafa Bilgic, Lilyana Mihalkova, Lise Getoor
KDD
2009
ACM
257views Data Mining» more  KDD 2009»
14 years 3 months ago
Argo: intelligent advertising by mining a user's interest from his photo collections
In this paper, we introduce a system named Argo which provides intelligent advertising made possible from users’ photo collections. Based on the intuition that user-generated ph...
Xin-Jing Wang, Mo Yu, Lei Zhang, Rui Cai, Wei-Ying...