Open AccessProceedings Article
Multi-prototype support vector machine
Fabio Aiolli,Alessandro Sperduti +1 more
- 09 Aug 2003
- pp 541-546
TL;DR: A compact constrained quadratic problem is given and an efficient algorithm for its optimization that guarantees a local minimum of the objective function is suggested that helps to escape from local minima.
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Abstract: We extend multiclass SVM to multiple prototypes per class. For this framework, we give a compact constrained quadratic problem and we suggest an efficient algorithm for its optimization that guarantees a local minimum of the objective function. An annealed process is also proposed that helps to escape from local minima. Finally, we report experiments where the performance obtained using linear models is almost comparable to that obtained by state-of-art kernel-based methods but with a significant reduction (of one or two orders) in response time.
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Citations
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Spatial tessellations. Concepts and Applications of Voronoi diagrams
Atsuyuki Okabe,Barry Boots,Kokichi Sugihara +2 more
- 01 Jan 1992
TL;DR: In this paper, the Voronoi diagram generalizations of the Voroni diagram algorithm for computing poisson Voroni diagrams are defined and basic properties of the generalization of Voroni's algorithm are discussed.
133
•Journal Article
Multiclass Classification with Multi-Prototype Support Vector Machines
Fabio Aiolli,Alessandro Sperduti +1 more
TL;DR: The multi-prototype SVM proposed in this paper extends multiclass SVM to multiple prototypes per class that allows to combine several vectors in a principled way to obtain large margin decision functions.
•Proceedings Article
Learning Preferences for Multiclass Problems
Fabio Aiolli,Alessandro Sperduti +1 more
- 01 Dec 2004
TL;DR: The Preference Learning Model is proposed as a unifying framework to model and solve a large class of multiclass problems in a large margin perspective and an original kernel-based method is proposed and evaluated on a ranking dataset with state-of-the-art results.
An Application of Morphological Feature Extraction and Support Vector Machines in Computerized ECG Interpretation
Wai Kei Lei,Bing Nan Li,Ming Chui Dong,Bin Bin Fu +3 more
- 04 Nov 2007
TL;DR: This paper presents a novel approach that recognizing heart rhythm with the combination of adaptive Hermite decomposition and support vector machines (SVM) classification, and the results confirm its reliability and accuracy of the proposed ECG interpreter.
16
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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Spatial Tessellations: Concepts and Applications of Voronoi Diagrams
Atsuyuki Okabe,Barry Boots,Kokichi Sugihara +2 more
- 01 Jan 1992
TL;DR: In this article, the Voronoi diagram generalizations of the Voroni diagram algorithm for computing poisson Voroni diagrams are defined and basic properties of the generalization of Voroni's algorithm are discussed.
•Book
Machine Learning: Neural and Statistical Classification
Donald Michie,David Spiegelhalter,Charles C. Taylor,John A. Campbell +3 more
- 01 Jan 2009
TL;DR: A survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning can be found in this article, where the authors also discuss their own work.
2.6K