Support Vector Ordinal Regression using Privileged Information
نویسندگان
چکیده
We introduce a new methodology, called SVORIM+, for utilizing privileged information of the training examples, unavailable in the test regime, to improve generalization performance in ordinal regression. The privileged information is incorporated during the training by modelling the slacks through correcting functions for each of the parallel hyperplanes separating the ordered classes. The experimental results on several benchmark and time series datasets show that inclusion of the privileged information during training can boost the generalization performance significantly.
منابع مشابه
The Benefits of Modeling Slack Variables in SVMs
In this letter, we explore the idea of modeling slack variables in support vector machine (SVM) approaches. The study is motivated by SVM+, which models the slacks through a smooth correcting function that is determined by additional (privileged) information about the training examples not available in the test phase. We take a closer look at the meaning and consequences of smooth modeling of s...
متن کاملMetric learning for incorporating privileged information in prototype-based models
Prototype-based classification models, and particularly Learning Vector Quantization (LVQ) frameworks with adaptive metrics, are powerful supervised classification techniques with good generalization behaviour. This thesis proposes three advanced learning methodologies, in the context of LVQ, aiming at better classification performance under various classification settings. The first contributi...
متن کاملSupport Vector Learning for Ordinal Regression
We investigate the problem of predicting variables of ordinal scale. This taks is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. The approach presented is ba...
متن کاملRegression Models for Ordinal Data : AMachine Learning
In contrast to the standard machine learning tasks of classi cation and metric regression we investigate the problem of predicting variables of ordinal scale, a setting referred to as ordinal regression. The task of ordinal regression arises frequently in the social sciences and in information retrieval where human preferences play a major role. Also many multi{class problems are really problem...
متن کاملSupport Vector Ordinal Regression
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal optimizat...
متن کامل