The most common model of machine learning algorithms involves two life-stages, namely the learning stage and the application stage. The cost of human expertise makes difficult the labeling of large sets of data for the training of machine learning algorithms. In this paper, we propose to challenge this strict dichotomy in the life cycle while addressing the issue of labeling of data. We discuss...

introduction: using hospital databases is extremely depended on an accurate classification that is based on clinical coding. we aimed to determine the validity of procedural coding in teaching hospitals. methods: in this cross-sectional study, we selected 246 medical records from kashan hospitals in 1386 and recoded procedures. procedures, coders’ information, and documentation principals were ...

in the books written on rhetorics, figures of speech, and literary terms one may find different non-systematic classifications of puns. some of these methods have been translated from the arabic literary texts. some other classifications are based on phonetics and phonology. in this research after surveying the common methods of classification, it has been tried to put forward certain new scien...

Classifications of several gesture types are very helpful in several applications. This paper tries to address fast classifications of hand gestures using DTW over multi-core simple processors. We presented a methodology to distribute templates over multi-cores and then allow parallel execution of the classification. The results were presented to voting algorithm in which the majority vote was ...

Naïve Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a novel graphical explanation facility for Naïve Bayes classifiers that serves three purposes. First, it transparently explains the ...

Understanding metadata written in natural language is a crucial requirement towards the successful automated integration of large scale, language-rich, classifications such as the ones used in digital libraries. In this article we analyze natural language labels used in such classifications by exploring their syntactic structure, and then we show how this structure can be used to detect pattern...

Many significant real-world classification tasks involve a large number of categories which are arranged in a hierarchical structure; for example, classifying documents into subject categories under the library of congress scheme, or classifying world-wide-web documents into topic hierarchies. We investigate the potential benefits of using a given hierarchy over base classes to learn accurate m...