Numerous problems in engineering and science can be converted into optimization problems. Artificial bee colony (ABC) algorithm is a newly developed stochastic optimization algorithm and has been widely used in many areas. However, due to the stochastic characteristics of its solution search equation, the traditional ABC algorithm often suffers from poor exploitation. Aiming at this weakness o...

the purpose of this study is to investigate the status of teaching and learning persian language across world universities. to this end, 10 respondents including university professors, lecturers and graduates of persian language from 8 first rank universities in asia, europe and america were interviewed. then from among 41 universities in 22 countries, 326 persian language learners were selecte...

In this section, we consider problems related to the topic of online learning. In particular, we are interested in problems where data is made available sequentially, and decisions must be made or actions taken based on the data currently available. This is to be contrasted with many problems in optimization and model fitting, where the data under consideration is available at the start. Furthe...

Many interesting multiclass problems can be cast in the general framework of label ranking defined on a given set of classes. The evaluation for such a ranking is generally given in terms of the number of violated order constraints between classes. In this paper, we propose the Preference Learning Model as a unifying framework to model and solve a large class of multiclass problems in a large m...

We present some problems with geometric characterizations that arise naturally in practical applications of machine learning. Our motivation comes from a well known machine learning problem, the problem of computing decision trees. Typically one is given a dataset of positive and negative points, and has to compute a decision tree that ts it. The points are in a low dimensional space, and the d...

Multi-armed bandits may be viewed as decompositionally-structured Markov decision processes (MDP's) with potentially very large state sets. A particularly elegant methodology for computing optimal policies was developed over twenty ago by Gittins Gittins & Jones, 1974]. Gittins' approach reduces the problem of nding optimal policies for the original MDP to a sequence of low-dimensional stopping...