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- Ricardo Ñanculef, Carlos Concha, Héctor Allende, Diego Candel, Claudio Moraga
- Int. J. Hybrid Intell. Syst.
- 2009

The margin maximization principle implemented by binary Support Vector Machines (SVMs) has been shown to be equivalent to find the hyperplane equidistant to the closest points belonging to the convex hulls that enclose each class of examples. In this paper, we propose an extension of SVMs for multicategory classification which generalizes this geometric… (More)

- Ricardo Ñanculef, Emanuele Frandi, Claudio Sartori, Héctor Allende
- Inf. Sci.
- 2014

Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as the Frank-Wolfe (FW) method. In particular, this procedure has been successfully applied to train large-scale instances of non-linear Support Vector Machines (SVMs). Specializing FW to SVM… (More)

Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale datasets. Traditional optimization methods cannot be directly applied in these cases, mainly due to memory restrictions. By adopting a slightly different objective function and… (More)

- Patricia Trejo, Ricardo Ñanculef, Héctor Allende, Claudio Moraga
- IWANN
- 2007

We work with a recently proposed algorithm where an ensemble of base classifiers, combined using weighted majority voting, is used for incremental classification of data. To successfully accommodate novel information without compromising previously acquired knowledge this algorithm requires an adequate strategy to determine the voting weights. Given an… (More)

- Stefano Lodi, Ricardo Ñanculef, Claudio Sartori
- SDM
- 2010

We explore a technique to learn Support Vector Models (SVMs) when training data is partitioned among several data sources. The basic idea is to consider SVMs which can be reduced to Minimal Enclosing Ball (MEB) problems in an feature space. Computation of such SVMs can be efficiently achieved by finding a coreset for the image of the data in the feature… (More)

- Héctor Allende, Emanuele Frandi, Ricardo Ñanculef, Claudio Sartori
- ArXiv
- 2013

- Ricardo Ñanculef, Carlos Concha, Claudio Moraga, Héctor Allende
- Fuzzy Days
- 2004

- Ricardo Ñanculef, Ilias N. Flaounas, Nello Cristianini
- Expert Syst. Appl.
- 2014

We present a method for the classification of multi-labelled text documents explicitly designed for data stream applications that require to process a virtually infinite sequence of data using constant memory and constant processing time. Our method is composed of an online procedure used to efficiently map text into a low-dimensional feature space and a… (More)