In the last few decades a computational approach to machine learning has emerged based on paradigms from recursion theory and the theory of computation. Such ideas include learning in the limit, learning by enumer-ation, and probably approximately correct (pac) learning. These models usually are not suitable in practical situations. In contrast, statistics based inference methods have enjoyed a...

In multiple criteria decision aid, preference disaggregation techniques are used to facilitate the construction of decision models, through regression-based approaches that enable the elicitation of preferential information from a representative set of decision examples provided by a decision-maker. The robustness of such approaches and their results is an important feature for their successful...

Existing fake website detection systems are unable to effectively detect fake websites. In this study, we advocate the development of fake website detection systems that employ classification methods grounded in statistical learning theory (SLT). Experimental results reveal that a prototype system developed using SLT-based methods outperforms seven existing fake website detection systems on a t...

In the last few decades a computational approach to machine learning has emerged based on paradigms from recursion theory and the theory of computation. Such ideas include learning in the limit, learning by enumeration, and probably approximately correct (pac) learning. These models usually are not suitable in practical situations. In contrast, statistics based inference methods have enjoyed a ...

Determining the context of a feature (i.e., the factors aaect-ing the ranking of a feature within a case) has been subject of several studies in analysis tasks, particularly for classiication but not in synthesis tasks like planning. In this paper we will address this problem and explain how the domain theory plays a key role in determining the context of a feature. We provide a characterizatio...

is the fraction of HEADS in X n = (X1, . . . , Xn). Since θ = Eθ̂n , (1) says that the sample (or empirical) average of the Xi ’s concentrates sharply around the statistical average θ = EX1. Bounds like these are fundamental in statistical learning theory. In the next few lectures, we will learn the techniques needed to derive such bounds for settings much more complicated than coin tossing. Thi...

Stock market forecasting has attracted so many researchers and investors that ‎many studies have been done in this field. These studies have led to the ‎development of many predictive methods, the most widely used of which are ‎machine learning-based methods. In machine learning-based methods, loss ‎function has a key role in determining the model weights. In this study a new loss ‎function is ...

artificial immune systems (ais) can be defined as soft computing systems inspired by immune system of vertebrates. immune system is an adaptive pattern recognition system. ais have been used in pattern recognition, machine learning, optimization and clustering. feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encoun...

conclusions considering the level of learning motivation among the students in aspects of self-efficacy conception, self-management, and communication abilities, it is recommended to provide programs to develop these characteristics by cooperative learning method based on social constructivism theory. patients and methods this descriptive cross-sectional study was conducted on the rehabilitatio...

the purpose of this study was to determine the relationship between employees’ empowerment and organizational learning in physical education organization. the research used a descriptive correlation method. statistical population consisted of all employees in physical education organization (n=722) and based on cochran sampling method, 250 subjects were selected for the statistical sample. the ...