today, students spend only 10 percent of their time in training centers and the major part of their time is spent in home, society and other places. however, significant events happen in this limited time. in addition to training and learning, they are prepared and educated to interact with society. education is an important issue in global educating and training systems. there are different ap...

We propose a distribution-based pruning of n-gram backoff language models. Instead of the conventional approach of pruning n-grams that are infrequent in training data, we prune n-grams that are likely to be infrequent in a new document. Our method is based on the n-gram distribution i.e. the probability that an n-gram occurs in a new document. Experimental results show that our method performe...

investigation of the interrelationships between yield and its components will improve the efficiency of a breeding program with appropriate selection criteria. in this study, the relationship among yield components and their direct and indirect influences on the total yield of melon were investigated. the study was based on evaluation of 49 entries gener-ated from a 7×7 diallel involving irania...

Own-rooted and phylloxera-free vines have been cultivated on the volcanic soil of Santorini for thousands years. All this time, by using two traditional training systems, ‘Κouloura’, ‘Κladeftiko’, which are well-adapted to specific climatic conditions island. This first report aims share scarce existing knowledge Santorini’s pruning looking at some important quantitative qualitative ripening pa...

A criterion for pruning parameters from N-gram backoff language models is developed, based on the relative entropy between the original and the pruned model. It is shown that the relative entropy resulting from pruning a single N-gram can be computed exactly and efficiently for backoff models. The relative entropy measure can be expressed as a relative change in training set perplexity. This le...

Recent empirical studies revealed two surprising pathologies of several common decision tree pruning algorithms. First, tree size is often a linear function of training set size, even when additional tree structure yields no increase in accuracy. Second, building trees with data in which the class label and the attributes are independent often results in large trees. In both cases, the pruning ...

Recent empirical studies revealed two surprising pathologies of several common decision tree pruning algorithms. First, tree size is often a linear function of training set size, even when additional tree structure yields no increase in accuracy. Second, building trees with data in which the class label and the attributes are independent often results in large trees. In both cases, the pruning ...

This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than nec...

We describe techniques for evaluating rule-based expert systems by complexity-analyses. The degree of redundancy and superfluity in the knowledge base is measured by comparing its performance to pruned versions of itself. The pruning method, called weakest-link pruning, takes advantage of the structure of the rule-base to perform a cost-complexity pruning of the set of rules. Two large rule-bas...