@article{MouraoMirandaHardoonHahnetal.2011,
  author    = {Mour{\~a}o-Miranda, Janaina and Hardoon, David R. and Hahn, Tim and Marquand, Andre F. and Williams, Steve C.R. and Shawe-Taylor, John and Brammer, Michael},
  title     = {Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine},
  series = {NeuroImage},
  volume    = {58},
  journal   = {NeuroImage},
  number    = {3},
  doi       = {10.1016/j.neuroimage.2011.06.042},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-141412},
  pages     = {793-804},
  year      = {2011},
  abstract  = {Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52\% of patients as outliers. However among the patients classified as outliers 70\% did not respond to treatment and among those classified as non-outliers 89\% responded to treatment. In addition 89\% of the healthy controls were classified as non-outliers.},
  language  = {en}
}
@unpublished{Nassourou2011,
  author    = {Nassourou, Mohamadou},
  title     = {Using Machine Learning Algorithms for Categorizing Quranic Chaptersby Major Phases of Prophet Mohammad's Messengership},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-66862},
  year      = {2011},
  abstract  = {This paper discusses the categorization of Quranic chapters by major phases of Prophet Mohammad's messengership using machine learning algorithms. First, the chapters were categorized by places of revelation using Support Vector Machine and na{\"i}ve Bayesian classifiers separately, and their results were compared to each other, as well as to the existing traditional Islamic and western orientalists classifications. The chapters were categorized into Meccan (revealed in Mecca) and Medinan (revealed in Medina). After that, chapters of each category were clustered using a kind of fuzzy-single linkage clustering approach, in order to correspond to the major phases of Prophet Mohammad's life. The major phases of the Prophet's life were manually derived from the Quranic text, as well as from the secondary Islamic literature e.g hadiths, exegesis. Previous studies on computing the places of revelation of Quranic chapters relied heavily on features extracted from existing background knowledge of the chapters. For instance, it is known that Meccan chapters contain mostly verses about faith and related problems, while Medinan ones encompass verses dealing with social issues, battles…etc. These features are by themselves insufficient as a basis for assigning the chapters to their respective places of revelation. In fact, there are exceptions, since some chapters do contain both Meccan and Medinan features. In this study, features of each category were automatically created from very few chapters, whose places of revelation have been determined through identification of historical facts and events such as battles, migration to Medina…etc. Chapters having unanimously agreed places of revelation were used as the initial training set, while the remaining chapters formed the testing set. The classification process was made recursive by regularly augmenting the training set with correctly classified chapters, in order to classify the whole testing set. Each chapter was preprocessed by removing unimportant words, stemming, and representation with vector space model. The result of this study shows that, the two classifiers have produced useable results, with an outperformance of the support vector machine classifier. This study indicates that, the proposed methodology yields encouraging results for arranging Quranic chapters by phases of Prophet Mohammad's messengership.},
  subject      = {Koran},
  language  = {en}
}