containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier’s probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one

Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for high-dimensional sparse data commonly encountered in applications like text classification, word-sense disambiguation, and drug design. These applications involve a large number of examples n

A method is described for simultaneous test construction using the Operations Research technique zero-one programming. The model for zero-one programming consists of two parts. The first contains the objective function that describes the aspect to be optimized. The second part contains

for solving the representative set selection problem do not guarantee that obtained representative sets are optimal (i.e. minimal). The enhanced zero–one optimal path set selection method [C.G. Chung, J.G. Lee, An enhanced zero–one optimal path set selection method, Journal of Systems and Software, 39

In this work, we study continuous reformulations of zero–one programming problems. We prove that, under suitable conditions, the optimal solutions of a zero–one programming problem can be obtained by solving a specific continuous problem.

Abstract We describe and analyze a new algorithm for agnostically learning kernel-based halfspaces with respect to the zero-one loss function. Unlike most previous formulations which rely on surrogate convex loss functions (e.g. hinge-loss in SVM and log-loss in logistic regression), we provide

/M, 2/M,..., (M − 1)/M, 1} and zn(M) is the optimal objective function value. 2β0 Assuming that M is large, zn(M) approximately equals to

are discrete random variables which are distributed uniformly in {1/M, 2/M,...,(M − 1)/M, 1}. zn(M) is the total profit to be maximized. Assuming that M is large, it is found that the optimal profit zn(M) is approximately equal to 2β0/3(1 − 0.3062 ( √ β0M) −1). An example from auction is used to explain

Unconstrained zero-one quadratic maximization problems can be solved in polynomial time when the symmetric matrix describing the objective function is positive semidefinite of fixed rank with known spectral decomposition.

. Specifically, we show that a zero-forcing beamforming (ZFBF) strategy, while generally suboptimal, can achieve the same asymptotic sum capacity as that of DPC, as the number of users goes to infinity. In proving this asymptotic result, we provide an algorithm for determining which users should be active under