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STUDENT ACHIEVEMENT INDICATORS AT DEFENSE LANGUAGE INSTITUTE FOREIGN LANGUAGE CENTER
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| 2 |
Automated topographic prominence based quantitative assessment of speech timing in Cerebellar Ataxia
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| 3 |
A new argument for co-active parses during language comprehension.
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In: Journal of experimental psychology. Learning, memory, and cognition, vol 45, iss 7 (2019)
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Psychometric properties of the Behavioural Outcomes of Anxiety questionnaire in stroke patients with aphasia.
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| 5 |
Symptoms Have Modest Accuracy in Detecting Endoscopic and Histologic Remission in Adults With Eosinophilic Esophagitis.
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In: Gastroenterology, vol. 150, no. 3, pp. 581-590.e4 (2016)
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| 6 |
Developmental Dyslexia and Phonological Processing in European Portuguese Orthography
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| 7 |
Fusion analysis of functional MRI data for classification of individuals based on patterns of activation.
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In: Brain and Mind Institute Researchers' Publications (2015)
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Validation of the Chinese version of the NUCOG cognitive screening tool in patients with epilepsy, dementia and other neurological disorders
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| 9 |
WISC-III Cognitive Profiles in Children with Developmental Dyslexia: Specific Cognitive Disability and Diagnostic Utility
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Working Memory in Portuguese Children With Developmental Dyslexia
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| 11 |
Formação de indicadores para a psicopatologia do Luto ; Training indicators for the psychopathology of mourning
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| 12 |
Detecting grammatical errors with treebank-induced, probabilistic parsers
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In: Wagner, Joachim orcid:0000-0002-8290-3849 (2012) Detecting grammatical errors with treebank-induced, probabilistic parsers. PhD thesis, Dublin City University. (2012)
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| 13 |
Impairment not only in remembering but also in knowing previously seen faces and words in schizophrenia.
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In: ISSN: 0165-1781 ; EISSN: 1872-7123 ; Psychiatry Research ; https://hal.archives-ouvertes.fr/hal-00654011 ; Psychiatry Research, Elsevier, 2011, 188 (1), pp.18-23. ⟨10.1016/j.psychres.2010.12.020⟩ (2011)
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| 14 |
Determinants of generic drug substitution in Switzerland.
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In: BMC Health Services Research, vol. 11, pp. art. 17 [12 p.] (2011)
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| 15 |
Detection of simulated memory impairment in 6- to 11-year-old children
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| 16 |
Functional Response in Ventral Temporal Cortex Differentiates Mild Cognitive Impairment from Normal Aging
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In: Neuroscience Faculty Publications (2010)
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| 17 |
Monothermal Caloric Screening Test Performance: A Relative Operating Characteristic Curve Analysis
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In: ETSU Faculty Works (2009)
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| 18 |
“Monothermal caloric screening test performance: A relative operating characteristic (ROC) curve analysis
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In: ETSU Faculty Works (2009)
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| 19 |
Lexical and semantic fluency discrepancy scores in aMCI and early Alzheimer's disease.
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In: Symplectic Elements at Oxford ; Europe PubMed Central ; PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) ; Web of Science (Lite) (http://apps.webofknowledge.com/summary.do) ; Scopus (http://www.scopus.com/home.url) ; CrossRef (2009)
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| 20 |
On classification, ranking, and probability estimation
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Flach, Peter; Matsubara, Edson. - : Dagstuhl Seminar Proceedings. 07161 - Probabilistic, Logical and Relational Learning - A Further Synthesis, 2008
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Abstract:
Given a binary classification task, a ranker is an algorithm that can sort a set of instances from highest to lowest expectation that the instance is positive. In contrast to a classifier, a ranker does not output class predictions – although it can be turned into a classifier with help of an additional procedure to split the ranked list into two. A straightforward way to compute rankings is to train a scoring classifier to assign numerical scores to instances, for example the predicted odds that an instance is positive. However, rankings can be computed without scores, as we demonstrate in this paper. We propose a lexicographic ranker, LexRank ; whose rankings are derived not from scores, but from a simple ranking of attribute values obtained from the training data. Although various metrics can be used, we show that by using the odds ratio to rank the attribute values we obtain a ranker that is conceptually close to the naive Bayes classifier, in the sense that for every instance of LexRank there exists an instance of naive Bayes that achieves the same ranking. However, the reverse is not true, which means that LexRank is more biased than naive Bayes. We systematically develop the relationships and differences between classification, ranking, and probability estimation, which leads to a novel connection between the Brier score and ROC curves. Combining LexRank with isotonic regression, which derives probability estimates from the ROC convex hull, results in the lexicographic probability estimator LexProb.
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Keyword:
calibration; Data processing Computer science; General Literature; probability estimation; Ranking; ROC analysis
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URN:
urn:nbn:de:0030-drops-13828
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URL: https://drops.dagstuhl.de/opus/volltexte/2008/1382/
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