Forest types mapping, is one of the most necessary elements in the forest management and silviculture treatments. Traditional methods such as field surveys are almost time-consuming and cost-intensive. Improvements in remote sensing data sources and classification –estimation methods are preparing new opportunities for obtaining more accurate forest biophysical attributes maps. This research co...

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 extended Min-Cost-Homs and a classification of their complexity w...

Proof. The proof is similar to the one in (Beygelzimer et al., 2008), which is based on defining the overallregret of any subtree. The key change in our proof is to define the path-regret of any subtree to be the total regret of the nodes on the ideal path of the subtree. The induction step follows similarly from the proof in (Beygelzimer et al., 2008) by considering two cases: one for the idea...

We consider the inverse problem in classification systems described as follows. Given a set of prototype cases representing a set of categories, a similarity function, and a new case classified in some category, find the cost minimizing changes to the attribute values such that the case is reclassified as a member of a (different) preferred category. The problem is “inverse” because the usual m...

A problem arises in data mining, when classifying unbalanced datasets using Support Vector Machines. Because of the uneven distribution and the soft margin of the classifier, the algorithm tries to improve the general accuracy of classifying a dataset, and in this process it might misclassify a lot of weakly represented classes, confusing their class instances as overshoot values that appear in...

The motivation of this paper is based on a hypothesis that non-linear decision nodes provide a better classification performance than axis-parallel decision nodes do in many practical problems, such as image classification, and voice classification. The algorithm – MNCS_DT is introduced in this paper to create non-linear splits nodes by novel discriminant analysis in decision tree for multi-cla...

Most traditional classification methods for Alzheimer’s disease (AD) aim to obtain a high accuracy, or equivalently a low classification error rate, which implicitly assumes that the losses of all misclassifications are the same. However, in practical AD diagnosis, the losses of misclassifying healthy subjects and AD patients are usually very different. For example, it may be troublesome if a h...

Boosting algorithms have been widely used to tackle a plethora of problems. In the last few years, a lot of approaches have been proposed to provide standard AdaBoost with cost-sensitive capabilities, each with a different focus. However, for the researcher, these algorithms shape a tangled set with diffuse differences and properties, lacking a unifying analysis to jointly compare, classify, ev...

A deterministic annealing algorithm for the design of tiedmixture HMM recognizers is proposed, which reduces the training sensitivity to parameter initialization, automatically smoothes the classification error cost function to allow gradientbased optimization, and seeks better solutions than known techniques. The new approach introduces randomness into the classification rule during the traini...