In this paper we study the computational complexity of the (extended) minimum cost homomorphism problem (Min-Cost-Hom) as a function of a constraint language, i.e. a set of constraint relations and cost functions that are allowed to appear in instances. A wide range of natural combinatorial optimisation problems can be expressed as Min-Cost-Homs and a classification of their complexity would be...

Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artif...

The standard prediction process of SVM requires acquisition of all the feature values for every instance. In practice, however, a cost is associated with the mere act of acquisition of a feature, e.g. CPU time needed to compute the feature out of raw data, the dollar amount spent for gleaning more information, or the patient wellness sacrificed by an invasive medical test, etc. In such applicat...

In image classification problems, especially those involving tumor or precancerous lesion, we are usually faced with the situation in which the cost of mistakenly classifying samples in one class is much higher than that of the opposite mistake in the other class. Therefore it is essential to include cost information about classes in our classification methods. This paper applies a cost-sensiti...

We propose an acceleration technique for boosting classification without any loss of classification accuracy and apply it to a face detection task. In classification task, much effort has been spent on improving the classification accuracy and the computational cost of training. In addition to them, the computational cost of classification itself can be critical in several applications includin...

Nonnegative matrix factorization (NMF) is a common method in data mining that have been used in different applications as a dimension reduction, classification or clustering method. Methods in alternating least square (ALS) approach usually used to solve this non-convex minimization problem.  At each step of ALS algorithms two convex least square problems should be solved, which causes high com...

in this paper, besides of studying and reviewing the theoretical analysis of the trade liberalization and industrial growth, the effect of trade liberalization on productivity in the industrial sector of the iran`s economy are investigated. in this study, a translog cost function and sur model for the period of 1974-2006 are used in order to evaluate the trade liberalization variable. at first,...

background: myocardial infarction (mi) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. this study aimed to present an mi prediction model using classification data mining methods, which consider the imbalance nature of the problem. methods: we enrolled 455 healthy and 295 myocardial infarction cases of visitors to shahid madani specialized ...