to a new design criteria for linear classifiers . gested loss functions that consider

Variable margin losses for classifier design. Hamed Masnadi-Shirazi and Nuno

which maximize the margin of confidence of the classifier, are the method of

of incorporating new losses and margin definitions in a theoret- ically rigorous

Aug 30, 2011 . In the last few years, large margin classifiers like support vector . training set and

Margin-based loss functions. Generalize the concept of margin to a large class of

Variable margin losses for. classifier design. 10 . To act or not to act: modeling

Variable margin losses for classifier design. Hamed Masnadi-Shirazi. Statistical

Aug 3, 2011 . algorithm is the pruning process, in which a tree classifier ˆfT is . in Gey [2010],

Nov 8, 2011 . Primal variable w of linear SVM and feature selection. Reduced . Currently it

we have classified correctly, yif(xi) ≥ 0, and want the loss function V to be small

Variable margin losses for classifier design. 1576-1584. Electronic Edition ·

(SVMs)—most notably, a convex objective function based on the hinge loss, .

dently trains a classifier for each label (as is done in the . We propose a max-

the margin m h ≤. R. 2 m2. + 1. ■ R is given by the data itself. ■ Margin m can

Loss and Regularization designed for. Adiabatic Quantum . Loss L(w) controls

The key is in the introduction of slack variables (see optimization techniques .

using methods designed for linear classifiers. . . The representation in terms of

Aug 19, 2006 . sociating a scalar energy to each configuration of the variables. . graphs, and

. TaylorBoost: First and second-order boosting algorithms with explicit margin

design of a classifier. Boosting is a reliable tool for this design. Since the

Abstract:Information-maximization clustering learns a probabilistic classifier in an

variables (or features) that are most relevant to the classification task. . (herein

Paper : 'Boosting classifier cascades' accepted at NIPS 2010. Paper : 'Variable

. Accepted Papers | Media | Committees. Accepted Papers. (

dom variable (X, Y ), where the explanatory variable X takes values in a . permit

Variable margin losses for classifier design. NIPS'2010. pp.1576~1584 Cited By

Hamed Masnadi-Shirazi, Nuno Vasconcelos: Variable margin losses for classifier

Energy-Based Models (EBMs) capture dependencies between variables by as- .

The method introduces slack variables, ξi, which measure the degree of . Boser,

mentary issue of designing classification algorithms that can deal with more

corresponding extensions for latent variable models, in which training operates

The success of boosting and SVM classifiers is not surprising when looked at

and Bartlett & Tewari (2007) show that replacing the large-margin loss with some

"For an SVM the value of ε in the ε-insensitive loss function should also be

Mondays 12-1, Building EBU3, Room 4140. Date, Speaker(s) .

Variable margin losses for classifier design. Hamed Masnadi-Shirazi and Nuno

design criteria for linear classifiers when inputs . . complements of the input

Variable margin losses for classifier design. Hamed Masnadi-Shirazi, Nuno

large-margin structured output learning such as Max-. Margin . . non-standard

cellstr (dataset), Create cell array of strings from dataset array. dataset, Construct

Variable margin losses for classifier design. [DBLP_Link] [Online_Version]

We can think of the independent variables (in a regression equation) as defining

for classifier design called "General Loss Minimization." The formulation is based

general method for combining the classifiers generated on the binary problems,

Poster: Variable margin losses for classifier design. This is part of the Poster

32(1), 171-177, January 2010. ? IEEE [ps] [pdf] [dataset]. Variable margin losses

margin loss with some differentiable loss leads to conditional probability

Outline. Online learning Framework; Design principles of online learning

which maximize the margin of confidence of the classifier, are the method of .

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