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  • Computer Science
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  • 1
    Language: English
    In: Science China Information Sciences, 2014, Vol.57(3), pp.1-17
    Description: Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes, important influence factors, their effects, and their structural implications. In this article, we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific conferences. On a structural level, we focus on static and dynamic properties of the contact graphs. Also, we analyze the resulting community structure using state-of-the-art automatic community detection algorithms. Specifically, we analyze the evolution of contacts and communities over time to consider the stability of the respective communities. Furthermore, we assess different factors which have an influence on the quality of community prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.
    Keywords: social network analysis ; community detection ; face-to-face contact networks ; temporal networks, community stability
    ISSN: 1674-733X
    E-ISSN: 1869-1919
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  • 2
    Language: English
    In: Journal of Computing and Information Technology, 12/2010, Vol.18(4), p.317
    Description: The WissKI system provides a framework for ontology-based science communication and cultural heritage documentation. In many cases, the documentation consists of semi-structured data records with free text fields. Most references in the texts comprise of person and place names, as well as time specifications. We present the WissKI tools for semantic annotation using controlled vocabularies and formal ontologies derived from CIDOC Conceptual Reference Model (CRM). Current research deals with the annotations as building blocks for event recognition. Finally, we outline how the CRM helps to build bridges between documentation in different scientific disciplines. Adapted from the source document
    Keywords: Natural Language Processing (56550) ; Text Analysis (89100) ; Semantic Web (76830) ; Descriptive Linguistics; Computational/Mathematical Linguistics and Machine Translation ; Article;
    ISSN: 1330-1136
    E-ISSN: 1846-3908
    Source: CrossRef
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  • 3
    Language: French
    Description: The land-use planning in France, and specifically the making process of planning documents, evolve both in a distinct and common way due to the effects of the rationalization of public policies, the State devolution of power and the transformations of land management issues. The actors...
    Keywords: Computer Science ; Information Theory ; Rawification ; Authoring Process ; Interorganizational Collaboration ; Constitutive Communication ; Instrumentation ; Sensemaking ; Soils Artificialization ; Land Use Planning ; Communication Constitutive ; Collaboration Inter-Organisationnelle ; Processus D'Auteurisation ; Prescription ; Big Data ; Brutification ; Travail Du Sens ; Artificialisation Des Sols ; Aménagement Du Territoire
    Source: Hyper Article en Ligne (CCSd)
    Source: Hyper Article en Ligne Open Access (CCSd)
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  • 4
    Language: English
    In: ACM SIGKDD Explorations Newsletter, 09 November 2010, Vol.12(1), pp.49-57
    Description: Cross-validation is a mainstay for measuring performance and progress in machine learning. There are subtle differences in how exactly to compute accuracy, F-measure and Area Under the ROC Curve (AUC) in cross-validation studies. However, these details are not discussed in the literature, and incompatible methods are used by various papers and software packages. This leads to inconsistency across the research literature. Anomalies in performance calculations for particular folds and situations go undiscovered when they are buried in aggregated results over many folds and datasets, without ever a person looking at the intermediate performance measurements. This research note clarifies and illustrates the differences, and it provides guidance for how best to measure classification performance under cross-validation. In particular, there are several divergent methods used for computing F-measure, which is often recommended as a performance measure under class imbalance, e.g., for text classification domains and in one-vs.-all reductions of datasets having many classes. We show by experiment that all but one of these computation methods leads to biased measurements, especially under high class imbalance. This paper is of particular interest to those designing machine learning software libraries and researchers focused on high class imbalance.
    Keywords: Computer Science
    ISSN: 19310145
    E-ISSN: 1931-0153
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  • 5
    Book chapter
    Book chapter
    Cambridge University Press
    Language: English
    In: Scaling up Machine Learning
    Description: Facing a problem of clustering amultimillion-data-point collection, amachine learning practitioner may choose to apply the simplest clustering method possible, because it is hard to believe that fancier methods can be applicable to datasets of such scale. Whoever is about to adopt this approach...
    Keywords: Computer Science
    ISBN: 9780521192248
    ISBN: 0521192242
    Source: Cambridge Core All Books (Cambridge University Press)〈img src=https://exlibris-pub.s3.amazonaws.com/CUP%20logo%20%282%29.gif style="vertical-align:middle;margin-left:7px"〉
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  • 6
    Language: English
    In: Acta neurochirurgica, June 2013, Vol.155(6), pp.1095-100; discussion 1100
    Description: International guidelines for the management of unruptured intracranial aneurysms (UIAs) recommend observation in aneurysms 〈10 mm due to the estimated low risk of rupture. The aim of our study was analyse the data of recently treated patients with ruptured cerebral aneurysms with the special focus on size and configuration in view of the frequency scale in a daily routine setting. We reviewed the data of all patients with aneurysmal subarachnoid haemorrhage (SAH) during the last 24 months at our institution. Configuration and size of the aneurysms were measured. Clinical data were collected using the following classifications for analysis: Hunt and Hess (H&H), modified Rankin Scale (mRS) and Fisher classification. Data of 135 patients with aneurysmal SAH (98 women, 37 men; ratio 2.6:1) were analysed. Analysis showed that 19 aneurysms (14 %) were 〉10 mm (mean size, 19.2 mm) and 116 aneurysms (85.9 %) 10 mm), 18 as multi-lobar (n = 16 10 mm) and 5 as fusiform (n = 4 10 mm). Since the results of our study showed that the majority of the aneurysms are 〈10 mm (mean, 6.2 mm), it is justified to challenge the recommendations of the international guidelines in a daily routine setting. We believe that the published data are not convincing enough to play a guidance role in daily routine. Due to improving surgical and endovascular techniques with satisfying results and the high number of ruptured small aneurysms, we believe a change in attitude in management of small-sized aneurysms is needed. Further diagnostic models are needed to determine the risk of rupture of intracranial aneurysms properly to obtain adequate treatment for UIAs.
    Keywords: Aneurysm, Ruptured -- Surgery ; Intracranial Aneurysm -- Surgery ; Practice Guidelines As Topic -- Standards ; Subarachnoid Hemorrhage -- Surgery
    ISSN: 00016268
    E-ISSN: 0942-0940
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  • 7
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Lecture Notes in Computer Science, Machine Learning: ECML 2006: 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings, pp.377-388
    Description: This paper analyzes boosting in unscaled versions of ROC spaces, also referred to as PN spaces. A minor revision to AdaBoost ’s reweighting strategy is analyzed, which allows to reformulate it in terms of stratification, and to visualize the boosting process in nested PN spaces as known from divide-and-conquer rule learning. The analyzed confidence-rated algorithm is proven to take more advantage of its base models in each iteration, although also searching a space of linear discrete base classifier combinations. The algorithm reduces the training error quicker without lacking any of the advantages of original AdaBoost. The PN space interpretation allows to derive a lower-bound for the area under the ROC curve metric (AUC) of resulting ensembles based on the AUC after reweighting. The theoretical findings of this paper are complemented by an empirical evaluation on benchmark datasets.
    Keywords: Computer Science ; Artificial Intelligence (Incl. Robotics) ; Algorithm Analysis and Problem Complexity ; Mathematical Logic and Formal Languages ; Database Management ; Engineering ; Computer Science
    ISBN: 9783540453758
    ISBN: 354045375X
    Source: SpringerLink Books
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  • 8
    Conference Proceeding
    Conference Proceeding
    Language: English
    In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, 21 August 2005, pp.265-274
    Description: Subgroup discovery is a learning task that aims at finding interesting rules from classified examples. The search is guided by a utility function, trading off the coverage of rules against their statistical unusualness. One shortcoming of existing approaches is that they do not incorporate prior knowledge. To this end a novel generic sampling strategy is proposed. It allows to turn pattern mining into an iterative process. In each iteration the focus of subgroup discovery lies on those patterns that are unexpected with respect to prior knowledge and previously discovered patterns. The result of this technique is a small diverse set of understandable rules that characterise a specified property of interest. As another contribution this article derives a simple connection between subgroup discovery and classifier induction. For a popular utility function this connection allows to apply any standard rule induction algorithm to the task of subgroup discovery after a step of stratified resampling. The proposed techniques are empirically compared to state of the art subgroup discovery algorithms.
    Keywords: Prior Knowledge ; Sampling ; Subgroup Discovery ; Computer Science
    ISBN: 159593135X
    ISBN: 9781595931351
    Source: ACM Digital Library (Association for Computing Machinery)
    Source: KESLI (ACM Digital Library)
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  • 9
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Lecture Notes in Computer Science, Local Pattern Detection: International Seminar, Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers, pp.171-189
    Description: Subgroup discovery aims at finding interesting subsets of a classified example set that deviates from the overall distribution. The search is guided by a so-called utility function, trading the size of subsets (coverage) against their statistical unusualness. By choosing the utility function accordingly, subgroup discovery is well suited to find interesting rules with much smaller coverage and bias than possible with standard classifier induction algorithms. Smaller subsets can be considered local patterns, but this work uses yet another definition: According to this definition global patterns consist of all patterns reflecting the prior knowledge available to a learner, including all previously found patterns. All further unexpected regularities in the data are referred to as local patterns. To address local pattern mining in this scenario, an extension of subgroup discovery by the knowledge-based sampling approach to iterative model refinement is presented. It is a general, cheap way of incorporating prior probabilistic knowledge in arbitrary form into Data Mining algorithms addressing supervised learning tasks.
    Keywords: Computer Science ; Artificial Intelligence (Incl. Robotics) ; Files ; Algorithm Analysis and Problem Complexity ; Probability and Statistics in Computer Science ; Database Management ; Information Storage and Retrieval ; Engineering ; Computer Science
    ISBN: 9783540265436
    ISBN: 3540265430
    Source: SpringerLink Books
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  • 10
    In: Intelligent Data Analysis, 03/15/2007, Vol.11(1), pp.3-28
    Description: In many real-world classification tasks, data arrives over time and the target concept to be learned from the data stream may change over time. Boosting methods are well-suited for learning from data streams, but do not address this concept drift problem. This paper proposes a boosting-like method to train a classifier ensemble from data streams that naturally adapts to concept drift. Moreover, it allows to quantify the drift in terms of its base learners. Similar as in regular boosting, examples are re-weighted to induce a diverse ensemble of base models. In order to handle drift, the proposed method continuously re-weights the ensemble members based on their performance on the most recent examples only. The proposed strategy adapts quickly to different kinds of concept drift. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the data and are thus not suited for mining massive streams. The proposed algorithm has low computational costs.
    Keywords: Drift ; Streams ; Mathematical Models ; Algorithms ; Learning ; Classifiers ; Computational Efficiency ; Expert Systems ; Trains ; Classification ; Artificial Intelligence ; Windows (Intervals) ; Handles ; Empirical Analysis ; Mining ; Information Storage, Retrieval, and Analysis (Ci) ; Article;
    ISSN: 1088467X
    E-ISSN: 15714128
    Source: CrossRef
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