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» Applying Support Vector Machines to Imbalanced Datasets
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ICPR
2008
IEEE
14 years 2 months ago
Fast model selection for MaxMinOver-based training of support vector machines
OneClassMaxMinOver (OMMO) is a simple incremental algorithm for one-class support vector classification. We propose several enhancements and heuristics for improving model select...
Fabian Timm, Sascha Klement, Thomas Martinetz
IJCNN
2008
IEEE
14 years 2 months ago
Ranking and selecting clustering algorithms using a meta-learning approach
Abstract— We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candi...
Marcílio Carlos Pereira de Souto, Ricardo B...
NECO
2008
108views more  NECO 2008»
13 years 7 months ago
An SMO Algorithm for the Potential Support Vector Machine
We describe a fast Sequential Minimal Optimization (SMO) procedure for solving the dual optimization problem of the recently proposed Potential Support Vector Machine (P-SVM). The...
Tilman Knebel, Sepp Hochreiter, Klaus Obermayer
ISMIR
2005
Springer
166views Music» more  ISMIR 2005»
14 years 1 months ago
Song-Level Features and Support Vector Machines for Music Classification
Searching and organizing growing digital music collections requires automatic classification of music. This paper describes a new system, tested on the task of artist identifica...
Michael I. Mandel, Dan Ellis
ICML
2004
IEEE
14 years 8 months ago
Multi-task feature and kernel selection for SVMs
We compute a common feature selection or kernel selection configuration for multiple support vector machines (SVMs) trained on different yet inter-related datasets. The method is ...
Tony Jebara