نتایج جستجو برای: class learning
تعداد نتایج: 964652 فیلتر نتایج به سال:
Introduction: With the advent of information and communication technology, in recent decades, a new gate opened to human beings and all its biological dimensions, and created many changes in the field of education and learning. Accordingly, the purpose of this study is to investigate how changes have been made in how learners learn from the growth and advancement of technologies. Methods:...
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can b...
Dissemination Level PU Public x PP Restricted to other programme participants (including the commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)
Several works point out class imbalance as an obstacle on applying machine learning algorithms to real world domains. However, in some cases, learning algorithms perform well on several imbalanced domains. Thus, it does not seem fair to directly correlate class imbalance to the loss of performance of learning algorithms. In this work, we develop a systematic study aiming to question whether cla...
Introduction: The purpose of this study was to identify the missions and goals, content, tools, and functions of faculty development centers in world-class universities. Method: This study was conducted using qualitative approach and comparative comparison method and content analysis. Data were collected using the Times 2020 ranking site and sites of centers at world-class universities rated be...
Dissemination Level PU Public X PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)
The present paper investigates identification of indexed families L of recursively enumerable languages from good examples. We distinguish class preserving learning from good examples (the good examples have to be generated with respect to a hypothesis space having the same range as L) and class comprising learning from good examples (the good examples have to be selected with respect to a hypo...
A key open problem in reinforcement learning is to assure convergence when using a compact hypothesis class to approximate the value function. Although the standard temporal-difference learning algorithm has been shown to converge when the hypothesis class is a linear combination of fixed basis functions, it may diverge with a general (nonlinear) hypothesis class. This paper describes the Bridg...
نمودار تعداد نتایج جستجو در هر سال
با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید