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» Applying Support Vector Machines to Imbalanced Datasets
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WEBI
2007
Springer
14 years 3 months ago
K-SVMeans: A Hybrid Clustering Algorithm for Multi-Type Interrelated Datasets
Identification of distinct clusters of documents in text collections has traditionally been addressed by making the assumption that the data instances can only be represented by ...
Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee G...
ICANN
2007
Springer
14 years 3 months ago
Unbiased SVM Density Estimation with Application to Graphical Pattern Recognition
Abstract. Classification of structured data (i.e., data that are represented as graphs) is a topic of interest in the machine learning community. This paper presents a different,...
Edmondo Trentin, Ernesto Di Iorio
KES
2007
Springer
14 years 3 months ago
Time Discretisation Applied to Anomaly Detection in a Marine Engine
This paper introduces the problems associated with anomaly detection in a marine engine, and explains the benefits that the SAX representation brings to the field. Despite limita...
Ian Morgan, Honghai Liu, George Turnbull, David J....
KDD
2010
ACM
310views Data Mining» more  KDD 2010»
14 years 26 days ago
An integrated machine learning approach to stroke prediction
Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early...
Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Ku...
NIPS
2000
13 years 10 months ago
A Support Vector Method for Clustering
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladi...