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» Applying Support Vector Machines to Imbalanced Datasets
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IWANN
1999
Springer
13 years 12 months ago
Support Vector Machines for Multi-class Classification
Abstract: Support vector machines (SVMs) are primarily designed for 2-class classification problems. Although in several papers it is mentioned that the combination of K SVMs can b...
Eddy Mayoraz, Ethem Alpaydin
3DPVT
2006
IEEE
197views Visualization» more  3DPVT 2006»
13 years 11 months ago
Aerial LiDAR Data Classification Using Support Vector Machines (SVM)
We classify 3D aerial LiDAR scattered height data into buildings, trees, roads, and grass using the Support Vector Machine (SVM) algorithm. To do so we use five features: height, ...
Suresh K. Lodha, Edward J. Kreps, David P. Helmbol...
BIOCOMP
2006
13 years 9 months ago
Support Vector Machines for Predicting microRNA Hairpins
- microRNAs (miRNAs) are 20-22 nt noncoding RNAs which are rapidly emerging as crucial regulators of gene expression in plants and animals. Identification of the hairpins which yie...
Karol Szafranski, Molly Megraw, Martin Reczko, Art...
ECIR
2003
Springer
13 years 9 months ago
Representative Sampling for Text Classification Using Support Vector Machines
In order to reduce human efforts, there has been increasing interest in applying active learning for training text classifiers. This paper describes a straightforward active learni...
Zhao Xu, Kai Yu, Volker Tresp, Xiaowei Xu, Jizhi W...
NIPS
2003
13 years 9 months ago
Phonetic Speaker Recognition with Support Vector Machines
A recent area of significant progress in speaker recognition is the use of high level features—idiolect, phonetic relations, prosody, discourse structure, etc. A speaker not on...
William M. Campbell, Joseph P. Campbell, Douglas A...