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Redefining support vector machines with the ordered weighted average
(Elsevier, 2018)
In this work, the classical soft-margin Support Vector Machine (SVM) formulation is redefined with the inclusion of an Ordered Weighted Averaging (OWA) operator. In particular, the hinge loss function is rewritten as a ...
Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile
(Elsevier, 2013)
In this work we apply multi-class support vector machines (SVMs) and a multi-class stochastic SVM formulation
to the classification of fish schools of three species: anchovy, common sardine, and Jack Mackerel,
and we ...
Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines
(Palgrave Macmillan Ltd., 2017)
Support vector machines (SVMs) have been successfully used to identify individuals' preferences in conjoint analysis. One of the challenges of using SVMs in this context is to properly control for preference heterogeneity ...
Advanced conjoint analysis using feature selection via support vector machines
(Elsevier, 2015)
One of the main tasks of conjoint analysis is to identify consumer preferences about potential products or services. Accordingly, different estimation methods have been proposed to determine the corresponding relevant ...
Automatic Feature Scaling and Selection for Support Vector Machine Classi cation with Functional Data
(Springer, 2020)
Functional Data Analysis (FDA) has become a very important eld
in recent years due to its wide range of applications. However, there are several
real-life applications in which hybrid functional data appear, i.e., data ...
Profit-based feature selection using support vector machines - General framework and an application for customer retention
(Elsevier, 2015)
Churn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral ...
Profit-based churn prediction based on Minimax Probability Machines
(Elsevier, 2020)
In this paper, we propose three novel profit-driven strategies for churn prediction. Our proposals extend the ideas of the Minimax Probability Machine, a robust optimization approach for binary classification that maximizes ...
Traditional versus Novel Forecasting Techniques: How Much do We Gain?
(JOHN WILEY & SONS, 2008-11)
This article applies two novel techniques to forecast the value of US manufacturing, shipments over the period 1956-2000: wavelets and support vector machines (SVM). Wavelets have become increasingly popular ill the fields ...
Non stationary demand forecasting based on empirical mode decomposition and support vector machines
(IEEE, 2017)
A company performance strongly depends on its ability of delivering the right quantity of of a given product at the time customers require. Even though some demand forecasting techniques have been developed, they have ...
Feature selection for Support Vector Machines via Mixed Integer Linear Programming
(Elsevier, 2014)
The performance of classification methods, such as Support Vector Machines, depends
heavily on the proper choice of the feature set used to construct the classifier. Feature
selection is an NP-hard problem that has been ...