For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a SVMs based algorithm for ...

How should a political campaign maximize its chance of winning an election when voters can abstain? We analyze when it is optimal for the campaign to maximize its expected margin of victory (EMV) over its opponent, i.e., the difference between its vote share of the pool of potential voters and its opponent’s share. If campaign decisions are decentralized to independent localities, EMV maximizat...

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a quadratic programming bas...

Theoretical spatial models of electoral voting tend to predict either convergence to an electoral mean (when voting is probabilistic) or chaos (when voting is deterministic). Here, we construct an empirical model of voting for the Israeli Knesset in 1992 (based on a large electoral sample and on analysis of party declarations). The probabilistic voting model so estimated fits the known election...

Margin maximization in the hard-margin sense, proposed as feature elimination criterion by the MFE-LO method, is combined here with data radius utilization to further aim to lower generalization error, as several published bounds and bound-related formulations pertaining to lowering misclassification risk (or error) pertain to radius e.g. product of squared radius and weight vector squared norm...

In this paper we describe and analyze LOCMEDBOOST, an algorithm that boosts regressors with input dependent weights. The algorithm is a synthesis of median boosting [1] and localized boosting [2, 3, 4], and unifies the advantages of the two approaches. We prove boostingtype convergence of the algorithm and give clear conditions for the convergence of the robust training error, where robustness ...

In many discrimination problems a large amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost . We extend the margin definition to unlabeled data and develop the gradient descent algorithm tha...

The support vector machine (SVM) is a widely used tool for classification. Although commonly understood as a method of finding the maximum-margin hyperplane, it can also be formulated as a regularized function estimation problem, corresponding to a hinge loss function plus an l2-norm regulation term. The doubly regularized support vector machine (DrSVM) is a variant of the standard SVM, which i...

We introduce a method to learn a mixture of submodular “shells” in a large-margin setting. A submodular shell is an abstract submodular function that can be instantiated with a ground set and a set of parameters to produce a submodular function. A mixture of such shells can then also be so instantiated to produce a more complex submodular function. What our algorithm learns are the mixture weig...

Optimization by simulation of agricultural practices can help to improve irrigation water use efficiency. This work introduces an efficient hierarchical decomposition method to design irrigation management strategies that is modelled as a continuous stochastic problem. Various combinations of selection (greedy, Pareto-based), division (middle, pivot, maximization) and evaluation techniques (glo...