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» On the generalization of soft margin algorithms
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ICANN
2007
Springer
14 years 1 months ago
Selection of Basis Functions Guided by the L2 Soft Margin
Support Vector Machines (SVMs) for classification tasks produce sparse models by maximizing the margin. Two limitations of this technique are considered in this work: firstly, th...
Ignacio Barrio, Enrique Romero, Lluís Belan...
TSP
2008
86views more  TSP 2008»
13 years 6 months ago
Fixed-Complexity Soft MIMO Detection via Partial Marginalization
Abstract--This paper presents a new approach to soft demodulation for MIMO channels. The proposed method is an approximation to the exact a posteriori probability-per-bit computer....
Erik G. Larsson, Joakim Jalden
ML
2002
ACM
167views Machine Learning» more  ML 2002»
13 years 6 months ago
Linear Programming Boosting via Column Generation
We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as...
Ayhan Demiriz, Kristin P. Bennett, John Shawe-Tayl...
PKDD
2010
Springer
152views Data Mining» more  PKDD 2010»
13 years 5 months ago
Online Knowledge-Based Support Vector Machines
Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and the...
Gautam Kunapuli, Kristin P. Bennett, Amina Shabbee...
CP
2000
Springer
13 years 11 months ago
Constraint Propagation for Soft Constraints: Generalization and Termination Conditions
Soft constraints based on semirings are a generalization of classical constraints, where tuples of variables' values in each soft constraint are uniquely associated to element...
Stefano Bistarelli, Rosella Gennari, Francesca Ros...