منابع مشابه
Learning to integrate web taxonomies
We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects ...
متن کاملUser Similarity from Linked Taxonomies: Subjective Assessments of Items
Subjective assessments (SAs) are assigned by users against items, such as ’elegant’ and ’gorgeous’, and are common in reviews/tags in many online-sites. However, previous studies fail to effectively use SAs for improving recommendations because few users rate the same items with the same SAs, which triggers the sparsity problem in collaborative filtering. We propose a novel algorithm that links...
متن کاملA guide to writing ABO test items.
Credentialing examinations are frequently the primary method for determining a candidate’s competency before entering a profession or achieving certification. “Credentialed” implies that the certificate holder is sufficiently competent to ensure that the health, welfare, and safety of the public are protected. The testing process is an outcome assessment tool that measures areas of mandatory an...
متن کاملLearning Taxonomies by Dependence Maximization
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, tak...
متن کاملTRIPPER: Rule Learning Using Taxonomies
In many application domains, there is a need for learning algorithms that generate accurate as well as comprehensible classifiers. In this paper, we present TRIPPER a rule induction algorithm that extends RIPPER, a widely used rule-learning algorithm. TRIPPER exploits knowledge in the form of taxonomies over the values of features used to describe data. We compare the performance of TRIPPER wit...
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ژورنال
عنوان ژورنال: Academic Medicine
سال: 1996
ISSN: 1040-2446
DOI: 10.1097/00001888-199610000-00036