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CVBIA
2005
Springer
14 years 3 months ago
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
Abstract. Markov Random Fields (MRFs) are a popular and wellmotivated model for many medical image processing tasks such as segmentation. Discriminative Random Fields (DRFs), a dis...
Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bi...
ISMIR
2005
Springer
145views Music» more  ISMIR 2005»
14 years 3 months ago
An Investigation of Feature Models for Music Genre Classification Using the Support Vector Classifier
In music genre classification the decision time is typically of the order of several seconds, however, most automatic music genre classification systems focus on short time feat...
Anders Meng, John Shawe-Taylor
ICANN
2005
Springer
14 years 3 months ago
Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines
Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetti...
Zeki Erdem, Robi Polikar, Fikret S. Gürgen, N...
JCDL
2003
ACM
160views Education» more  JCDL 2003»
14 years 3 months ago
Automatic Document Metadata Extraction Using Support Vector Machines
Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadat...
Hui Han, C. Lee Giles, Eren Manavoglu, Hongyuan Zh...
COLT
1999
Springer
14 years 2 months ago
Covering Numbers for Support Vector Machines
—Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel function. Until recently, the only bounds on th...
Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Ro...