We consider Constraint Satisfaction Problems in which constraints can be initially incomplete, where it is unknown whether certain tuples satisfy the constraint or not. We assume that we can determine such an unknown tuple, i.e., find out whether this tuple is in the constraint or not, but doing so incurs a known cost, which may vary between tuples. We also assume that we know the probability o...

We compare tractable classes of constraint satisfaction problems (CSPs). We first give a uniform presentation of the major structural CSP decomposition methods. We then introduce a new class of tractable CSPs based on the concept of hypertree decomposition recently developed in Database Theory, and analyze the cost of solving CSPs having bounded hypertree-width. We provide a framework for compa...

Support vector machine (SVM) is an important and fundamental technique in learning. Soft-margin SVM models have stronger generalization performance compared with the hard-margin SVM. Most existing works use hinge-loss function which can be regarded as upper bound of 0–1 loss function. However, it cannot explicitly control number misclassified samples. In this paper, we idea soft-margin propose ...

this work focuses on the correction of both the coecient and the right hand side matrices of the inconsistent matrix equations $ax = b$ and $xc = d$ with orthogonal constraint. by optimal correction approach, a general representation of the orthogonal solution is obtained. this method is tested on two examples to show that the optimal correction is eective and highly accurate.