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Journal articles on the topic 'Structured data'

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Published: 4 June 2021

Last updated: 11 March 2023

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1

Roukema, J., A. M. van Ginneken, M. de Wilde, J. van der Lei, and R. K. Los. "Are Structured Data Structured Identically?" Methods of Information in Medicine 44, no. 05 (2005): 631–38. http://dx.doi.org/10.1055/s-0038-1634019.

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Summary Objective: OpenSDE is an application that supports structured recording of narrative patient data to enable use of the data in both clinical practice and clinical research. Reliability and accuracy of collected data are essential for subsequent data use. In this study we analyze the uniformity of data entered with OpenSDE. Our objective is to obtain insight into the consensus and differences of recorded data. Methods: Three pediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared and all recorded findings were classified into one of six categories of difference. Results: Of all findings 22% were recorded identically; 17% of the findings were recorded differently (predominantly as free text); 61% was omitted, inferred, or in conflict with the paper record. Conclusion: The results of this study show that recording patient data using structured data entry does not necessarily lead to uniformly structured data.

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2

Kunz, Donald L., and A. Stewart Hopkins. "Structured data in structural analysis software." Computers & Structures 26, no. 6 (January 1987): 965–78. http://dx.doi.org/10.1016/0045-7949(87)90114-3.

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3

Zhang, Y. "Open-access and Structured Data in Drug Discovery." Biomedical Data Journal 01, no. 1 (January 2015): 39–41. http://dx.doi.org/10.11610/bmdj.01107.

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4

Shin, Kilho, and Dave Shepard. "Morphism-Based Learning for Structured Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5767–75. http://dx.doi.org/10.1609/aaai.v34i04.6033.

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In mathematics, morphism is a term that indicates structure-preserving mappings between mathematical structures of the same type. Linear transformations for linear spaces, homomorphisms for algebraic structures and continuous functions for topological spaces are examples. Many data researched in machine learning, on the other hand, can include mathematical structures in them. Strings are totally ordered sets, and trees can be understood not only as graphs but also as partially ordered sets with respect to an ancestor-to-descendent order and semigroups with respect to the binary operation to determine nearest common ancestor. In this paper, we propose a generic and theoretic framework to investigate similarity of structured data through structure-preserving one-to-one partial mappings, which we call morphisms. Through morphisms, useful and important methods studied in the literature can be abstracted into common concepts, although they have been studied separately. When we study new structures of data, we will be able to extend the legacy methods for the purpose of studying the new structure, if we can define morphisms properly. Also, this view reveals hidden relations between methods known in the literature and can let us understand them more clearly. For example, we see that the center star algorithm, which was originally developed to compute sequential multiple alignments, can be abstracted so that it not only applies to data structures other than strings but also can be used to solve problems of pattern extraction. The methods that we study in this paper include edit distance, multiple alignment, pattern extraction and kernel, but it is sure that there exist much more methods that can be abstracted within our framework.

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Malewski, Stefan, Michael Greenberg, and Éric Tanter. "Gradually structured data." Proceedings of the ACM on Programming Languages 5, OOPSLA (October 20, 2021): 1–29. http://dx.doi.org/10.1145/3485503.

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Dynamically-typed languages offer easy interaction with ad hoc data such as JSON and S-expressions; statically-typed languages offer powerful tools for working with structured data, notably algebraic datatypes , which are a core feature of typed languages both functional and otherwise. Gradual typing aims to reconcile dynamic and static typing smoothly. The gradual typing literature has extensively focused on the computational aspect of types, such as type safety, effects, noninterference, or parametricity, but the application of graduality to data structuring mechanisms has been much less explored. While row polymorphism and set-theoretic types have been studied in the context of gradual typing, algebraic datatypes in particular have not, which is surprising considering their wide use in practice. We develop, formalize, and prototype a novel approach to gradually structured data with algebraic datatypes. Gradually structured data bridges the gap between traditional algebraic datatypes and flexible data management mechanisms such as tagged data in dynamic languages, or polymorphic variants in OCaml. We illustrate the key ideas of gradual algebraic datatypes through the evolution of a small server application from dynamic to progressively more static checking, formalize a core functional language with gradually structured data, and establish its metatheory, including the gradual guarantees.

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6

Da San Martino, Giovanni, and Alessandro Sperduti. "Mining Structured Data." IEEE Computational Intelligence Magazine 5, no. 1 (February 2010): 42–49. http://dx.doi.org/10.1109/mci.2009.935308.

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7

Sorber, Laurent, Marc Van Barel, and Lieven De Lathauwer. "Structured Data Fusion." IEEE Journal of Selected Topics in Signal Processing 9, no. 4 (June 2015): 586–600. http://dx.doi.org/10.1109/jstsp.2015.2400415.

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8

Liu, Feng Hua. "Research on the Data Model and the Approaches to Data Mining in the Semi-Structured Data." Applied Mechanics and Materials 513-517 (February 2014): 663–66. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.663.

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As an important form of Internet data, semi-structured data in data mining is an important fist conditions. And the data mining was designed to find and extract large database in the implied information of value. This paper first introduced the half structured data concept characteristic, based on the data from each of the half structural said, the data model two half-and-half structured data model are introduced, finally summarizes semi-structured data model and the relationship between the data model before difference [1].

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9

Coolen, Henny. "Measurement and Analysis of Less Structured Data in Housing Research." Open House International 32, no. 3 (September 1, 2007): 55–65. http://dx.doi.org/10.1108/ohi-03-2007-b0007.

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Two ideal types of data can be distinguished in housing research: structured and less-structured data. Questionnaires and official statistics are examples of structured data, while less-structured data arise for instance from open interviews and documents. Structured data are sometimes labelled quantitative, while less-structured data are called qualitative. In this paper structured and less-structured data are considered from the perspective of measurement and analysis. Structured data arise when the researcher has an a priori category system or measurement scale available for collecting the data. When such an a priori system or scale is not available the data are called less-structured. It will be argued that these less-structured observations can only be used for any further analysis when they contain some minimum level of structure called a category system, which is equivalent to a nominal measurement scale. Once this becomes evident, one realizes that through the necessary process of categorization less-structured data can be analyzed in much the same way as structured data, and that the difference between the two types of data is one of degree and not of kind. In the second part of the paper these ideas are illustrated with examples from my own research on the meaning of preferences for dwelling features in which the concept of a meaning structure plays a central part. Until now these meaning structures have been determined by means of semi-structured interviews which, even with small samples, result in large amounts of less-structured data.

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10

Mastorci, F., and G. Iervasi. "The Need for Open-access, Structured Data in Endocrine Research." Biomedical Data Journal 01, no. 1 (January 2015): 33–35. http://dx.doi.org/10.11610/bmdj.01105.

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Pingitore, A., and C. Carpeggiani. "The Need for Open-access Structured Data in Cardiology Research." Biomedical Data Journal 01, no. 1 (January 2015): 36–38. http://dx.doi.org/10.11610/bmdj.01106.

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12

Monakhova, Tatyana Vyacheslavovna. "XML-structured data protection." SPIIRAS Proceedings 2, no. 25 (March 17, 2014): 182. http://dx.doi.org/10.15622/sp.25.8.

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13

Florescu, Daniela. "Managing Semi-Structured Data." Queue 3, no. 8 (October 2005): 18–24. http://dx.doi.org/10.1145/1103822.1103832.

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14

Balbi, Simona. "Analysis of Structured Data." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 68, no. 1 (October 2000): 84–85. http://dx.doi.org/10.1177/075910630006800129.

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15

Schulze, Philipp, Benjamin Unger, Christopher Beattie, and Serkan Gugercin. "Data-driven structured realization." Linear Algebra and its Applications 537 (January 2018): 250–86. http://dx.doi.org/10.1016/j.laa.2017.09.030.

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16

Korn, Flip, Barna Saha, Divesh Srivastava, and Shanshan Ying. "On repairing structural problems in semi-structured data." Proceedings of the VLDB Endowment 6, no. 9 (July 2013): 601–12. http://dx.doi.org/10.14778/2536360.2536361.

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17

Wu, P. D., Y. Yin, C. M. Li, and X. L. Liu. "AGGREGATION IN LAND-COVER DATA GENERALIZATION CONSIDERING SPATIAL STRUCTURE CHARACTERISTICS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W9 (September 30, 2019): 111–18. http://dx.doi.org/10.5194/isprs-annals-iv-4-w9-111-2019.

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Abstract. Aggregation is an important operation for the generalization of land-cover data. However, current research often entails aggregation on a global perspective, which is not conducive to capturing the spatial characteristics of geographic objects with significant spatial structures, i.e., structured geographic objects. Hence this paper proposes an area aggregation method that can maintain the boundary characteristics of the structured geographic objects. First, we identify the structured geographic objects based on the description parameters of the spatial structure. Second, a Miter-type buffer transformation is introduced to extract the boundary of each structured geographic object, and area elements inside the boundary are processed with corresponding aggregation operations. Finally, the boundary of the structured geographic objects and the aggregation result of the area elements are inserted back into the aggregated result of the original land-cover data using the NOT operation. The proposed approach is experimentally validated using geographical condition census data for a city in southern China. The experimental result indicates that the proposed approach not only reasonably identify the typical characteristics of structured geographic objects but also effectively maintains the boundary characteristics of these objects.

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18

Singh, Shashi Pal, Ajai Kumar, Rachna Awasthi, Neetu Yadav, and Shikha Jain. "Intelligent Bilingual Data Extraction and Rebuilding Using Data Mining for Big Data." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 513–18. http://dx.doi.org/10.1166/jctn.2020.8699.

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In today’s World there exists various source of data in various formats (file formats), different structure, different types and etc. which is a hug collection of unstructured over the internet or social media. This gives rise to categorization of data as unstructured, semi structured and structured data. Data that exist in irregular manner without any particular schema are referred as unstructured data which is very difficult to process as it consists of irregularities and ambiguities. So, we are focused on Intelligent Processing Unit which converts unstructured big data into intelligent meaningful information. Intelligent text extraction is a technique that automatically identifies and extracts text from file format. The system consists of different stages which include the pre-processing, keyphase extraction techniques and transformation for the text extraction and retrieve structured data from unstructured data. The system consists multiple method/approach give better result. We are currently working in various file formats and converting the file format into DOCX which will come in the form of the un-structure Form, and then we will obtain that file in the structure form with the help of intelligent Pre-processing. The pre-process stages that triggers the unstructured data/corpus into structured data converting into meaning full. The Initial stage is the system remove the stop word, unwanted symbols noisy data and line spacing. The second stage is Data Extraction from various sources of file or types of files into proper format plain text. The then in third stage we transform the data or information from one format to another for the user to understand the data. The final step is rebuilding the file in its original format maintaining tag of the files. The large size files are divided into sub small size file to executed the parallel processing algorithms for fast processing of larger files and data. Parallel processing is a very important concept for text extraction and with its help; the big file breaks in a small file and improves the result. Extraction of data is done in Bilingual language, and represent the most relevant information contained in the document. Key-phase extraction is an important problem of data mining, Knowledge retrieval and natural speech processing. Keyword Extraction technique has been used to abstract keywords that exclusively recognize a document. Rebuilding is an important part of this project and we will use the entire concept in that file format and in the last, we need the same format which we have done in that file. This concept is being widely used but not much work of the work has been done in the area of developing many functionalities under one tool, so this makes us feel the requirement of such a tool which can easily and efficiently convert unstructured files into structured one.

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19

Usharani, B. "Mapping the Semi-Structured Data to the Structured Data for Inverted Index Compression." International Journal of Database Theory and Application 10, no. 1 (January 31, 2017): 235–44. http://dx.doi.org/10.14257/ijdta.2017.10.1.22.

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20

Tran, Thanh, Gunter Ladwig, and Sebastian Rudolph. "Managing Structured and Semistructured RDF Data Using Structure Indexes." IEEE Transactions on Knowledge and Data Engineering 25, no. 9 (September 2013): 2076–89. http://dx.doi.org/10.1109/tkde.2012.134.

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21

Rachapudi, Nikitha, Lakshmipathy Ganesh, Abinaya Sekar, Anand K. S, and Rajkumar Sakthibalan. "Discovery of Structured Data Using Unsupervised Spatial Clustering and Human Supervision." International Journal of Machine Learning and Computing 9, no. 5 (October 2019): 586–91. http://dx.doi.org/10.18178/ijmlc.2019.9.5.844.

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22

Daci, Genti, and Megi Shyle. "Improving data integrity and performance of cryptographic log structured file systems." Applied Technologies and Innovations 5, no. 2 (November 1, 2011): 1–10. http://dx.doi.org/10.15208/ati.2011.8.

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23

Bunevicius, A. "The Need for Open-access, Structured Data in Clinical Brain Research." Biomedical Data Journal 01, no. 1 (January 2015): 27–32. http://dx.doi.org/10.11610/bmdj.01104.

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24

Stroo-Moredo, Elena, and Marnix Krikke. "Improving the Reuse of Design Data during the Tender Phase." Journal of Ship Production and Design 31, no. 02 (May 1, 2015): 67–78. http://dx.doi.org/10.5957/jspd.2015.31.2.67.

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This article presents a methodology to improve the reuse of design data in the tender phase. This methodology consists of the implementation of two novel structures: a Functional Breakdown Structure and a System Breakdown Structure. The first provides a tool to capture that key data, which is currently missing, for the reuse at an early stage of design. The second structure ensures that the design data are structured, documented, and easy accessible and retrievable for new tenders. The two structures are linked to ensure the traceability and reusability of design data for new tenders.

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25

Goel, Alexander K., Walter Scott Campbell, and Richard Moldwin. "Structured Data Capture for Oncology." JCO Clinical Cancer Informatics, no. 5 (February 2021): 194–201. http://dx.doi.org/10.1200/cci.20.00103.

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Lack of interoperability is one of the greatest challenges facing healthcare informatics. Recent interoperability efforts have focused primarily on data transmission and generally ignore data capture standardization. Structured Data Capture (SDC) is an open-source technical framework that enables the capture and exchange of standardized and structured data in interoperable data entry forms (DEFs) at the point of care. Some of SDC’s primary use cases concern complex oncology data such as anatomic pathology, biomarkers, and clinical oncology data collection and reporting. Its interoperability goals are the preservation of semantic, contextual, and structural integrity of the captured data throughout the data’s lifespan. SDC documents are written in eXtensible Markup Language (XML) and are therefore computer readable, yet technology agnostic—SDC can be implemented by any EHR vendor or registry. Any SDC-capable system can render an SDC XML file into a DEF, receive and parse an SDC transmission, and regenerate the original SDC form as a DEF or synoptic report with the response data intact. SDC is therefore able to facilitate interoperable data capture and exchange for patient care, clinical trials, cancer surveillance and public health needs, clinical research, and computable care guidelines. The usability of SDC-captured oncology data is enhanced when the SDC data elements are mapped to standard terminologies. For example, an SDC map to Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) enables aggregation of SDC data with other related data sets and permits advanced queries and groupings on the basis of SNOMED CT concept attributes and description logic. SDC supports terminology maps using separate map files or as terminology codes embedded in an SDC document.

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26

Goodenough, Sally. "Structured data, standards, and indexes." Indexer: The International Journal of Indexing 31, no. 4 (December 2013): 133–37. http://dx.doi.org/10.3828/indexer.2013.45.

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27

Singh, Mohini. "Structured Data and Electronic Filings." CFA Institute Magazine 27, no. 2 (June 2016): 62. http://dx.doi.org/10.2469/cfm.v27.n2.22.

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28

Badouel, Eric, Loïc Hélouët, and Christophe Morvan. "Petri Nets with Structured Data." Fundamenta Informaticae 146, no. 1 (September 13, 2016): 35–82. http://dx.doi.org/10.3233/fi-2016-1375.

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29

Plewis, I., and G. Raab. "Editorial: Modelling structured categorical data." Journal of the Royal Statistical Society: Series A (Statistics in Society) 162, no. 3 (January 1999): 269–71. http://dx.doi.org/10.1111/1467-985x.00134.

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30

Cafarella, Michael J., Alon Halevy, and Jayant Madhavan. "Structured data on the web." Communications of the ACM 54, no. 2 (February 2011): 72–79. http://dx.doi.org/10.1145/1897816.1897839.

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31

Henkel, Maurice, and Bram Stieltjes. "Structured Data Acquisition in Oncology." Oncology 98, Suppl. 6 (November 15, 2019): 423–29. http://dx.doi.org/10.1159/000504259.

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32

Batini, Carlo, Anisa Rula, Monica Scannapieco, and Gianluigi Viscusi. "From Data Quality to Big Data Quality." Journal of Database Management 26, no. 1 (January 2015): 60–82. http://dx.doi.org/10.4018/jdm.2015010103.

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This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review.

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33

Praveen, Shagufta. "Conversion of Unstructured to Structured: A Solution Using Data Science and NOSQL." Revista Gestão Inovação e Tecnologias 11, no. 4 (July 10, 2021): 1772–77. http://dx.doi.org/10.47059/revistageintec.v11i4.2235.

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Eisinger, Daniel, George Tsatsaronis, Alina Petrova, Efstathios Karanastasis, Vassiliki Andronikou, and Efthymios Chondrogiannis. "OSL Platform: A Link to Open-access Scientific Information and Structured Data." Biomedical Data Journal 01, no. 1 (January 2015): 52–54. http://dx.doi.org/10.11610/bmdj.01109.

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35

Kelkar, Bhagyashri A., and Dr S. F. Rodd. "A Review of Feature Selection Techniques for Clustering High Dimensional Structured Data." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 176–79. http://dx.doi.org/10.9756/bijsesc.8270.

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36

Husain, Moula, Somashekhar Patil, B. Indira, S. M. Meena, and D. G. Narayan. "Structured Approach of Designing Data Structure and Algorithms Laboratory Experiments." Journal of Engineering Education Transformations 29, no. 2 (October 1, 2015): 83. http://dx.doi.org/10.16920/jeet/2015/v29i2/83072.

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37

Hannemann, V. "Structured multigrid agglomeration on a data structure for unstructured meshes." International Journal for Numerical Methods in Fluids 40, no. 3-4 (2002): 361–68. http://dx.doi.org/10.1002/fld.292.

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38

Schromm, T., T. Frankewitsch, M. Giehl, F. Keller, and D. Zellner. "Structured Data Entry for Reliable Acquisition of Pharmacokinetic Data." Methods of Information in Medicine 35, no. 03 (May 1996): 261–64. http://dx.doi.org/10.1055/s-0038-1634673.

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Abstract:A pharmacokinetic database was constructed that is as free of errors as possible. Pharmacokinetic parameters were derived from the literature using a text-processing system and a database system. A random data sample from each system was compared with the original literature. The estimated error frequencies using statistical methods differed significantly between the two systems. The estimated error frequency in the text-processing system was 7.2%, that in the database system 2.7%. Compared with the original values in the literature, the estimated probability of error for identical pharmacokinetic parameters recorded in both systems is 2.4% and is not significantly different from the error frequency in the database. Parallel data entry with a text-processing system and a database system is, therefore, not significantly better than structured data entry for reducing the error frequency.

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39

Taggart, Jane, Siaw-Teng Liaw, and Hairong Yu. "Structured data quality reports to improve EHR data quality." International Journal of Medical Informatics 84, no. 12 (December 2015): 1094–98. http://dx.doi.org/10.1016/j.ijmedinf.2015.09.008.

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40

Scriney, Michael, Suzanne McCarthy, Andrew McCarren, Paolo Cappellari, and Mark Roantree. "Automating Data Mart Construction from Semi-structured Data Sources." Computer Journal 62, no. 3 (June 15, 2018): 394–413. http://dx.doi.org/10.1093/comjnl/bxy064.

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41

Chen, Qun, Andrew Lim, Kian Win Ong, and Ji Qing Tang. "Indexing graph-structured XML data for efficient structural join operation." Data & Knowledge Engineering 58, no. 2 (August 2006): 159–79. http://dx.doi.org/10.1016/j.datak.2005.05.008.

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42

Shliakhtina, E. A., and D. Y. Gamayunov. "Anomaly detection in JSON structured data." Prikladnaya Diskretnaya Matematika, no. 56 (2022): 83–103. http://dx.doi.org/10.17223/20710410/56/5.

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In this paper, we address the problem of intrusion detection for modern web applications and mobile applications with the cloud-based server side, using malicious content detection in JSON data, which is currently one of the most popular data serialization and exchange formats between client and server parts of an application. We propose a method for building a JSON model for the given set of JSON objects capable of detection of structure and type anomalies. The model is based on the models for basic data types inside JSON collection objects and schema model that generalizes objects’ structure in the collection. We performed experiments using modifications of objects’ structures and insertions of code injection attack vectors such as SQL injections, OS command injections, and JavaScript/HTML injections. The analysis showed statistical significance between the model’s predictions and the presence of anomalies in the data gathered from the real web applications’ traffic. The quality of the model’s predictions was measured using the Matthews correlation coefficient (MCC). The MCC values computed on the data were close to one which indicates the model’s high efficiency in solving the problem of anomaly detection in JSON objects.

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43

Maurya, Arvind, Yogesh Gupta, and Stuti Awasthi. "Insights Exploration of Structured and Unstructured Data and Construction of Automated Knowledge Banks." International Journal of Machine Learning and Computing 6, no. 2 (April 2016): 117–22. http://dx.doi.org/10.18178/ijmlc.2016.6.2.584.

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44

Moscoso Zea, Oswaldo. "Megastore: structured storage for Big Data." Enfoque UTE 3, no. 2 (December 31, 2012): 01–12. http://dx.doi.org/10.29019/enfoqueute.v3n2.1.

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Megastore es uno de los componentes principales de la infraestructura de datos de Google, elcual ha permitido el procesamiento y almacenamiento de grandes volúmenes de datos (BigData) con alta escalabilidad, confiabilidad y seguridad. Las compañías e individuos que usanestá tecnología se están beneficiando al mismo tiempo de un servicio estable y de altadisponibilidad. En este artículo se realiza un análisis de la infraestructura de datos de Google,comenzando por una revisión de los componentes principales que se han implementado en losúltimos años hasta la creación de Megastore. Se presenta también un análisis de los aspectostécnicos más importantes que se han implementado en este sistema de almacenamiento y que le han permitido cumplir con los objetivos para los que fue creado.Abstract:Megastore is one of the building blocks of Google’s data infrastructure. It has allowed storingand processing operations of huge volumes of data (Big Data) with high scalability, reliabilityand security. Companies and individuals using this technology benefit from a highly availableand stable service. In this paper an analysis of Google’s data infrastructure is made, startingwith a review of the core components that have been developed in recent years until theimplementation of Megastore. An analysis is also made of the most important

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45

Szeremeta, Łukasz, and Dominik Tomaszuk. "Generating molecular entities as structured data." SoftwareX 15 (July 2021): 100733. http://dx.doi.org/10.1016/j.softx.2021.100733.

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46

Barbosa, Luciano, Johny Moreira, and Everaldo Costa Neto. "Analysis of structured data on Wikipedia." International Journal of Metadata, Semantics and Ontologies 15, no. 1 (2021): 71. http://dx.doi.org/10.1504/ijmso.2021.10040250.

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47

Moreira, Johny, Everaldo Costa Neto, and Luciano Barbosa. "Analysis of structured data on Wikipedia." International Journal of Metadata, Semantics and Ontologies 15, no. 1 (2021): 71. http://dx.doi.org/10.1504/ijmso.2021.117108.

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48

Yaghmazadeh, Navid, Christian Klinger, Isil Dillig, and Swarat Chaudhuri. "Synthesizing transformations on hierarchically structured data." ACM SIGPLAN Notices 51, no. 6 (August 2016): 508–21. http://dx.doi.org/10.1145/2980983.2908088.

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Biehl, Michael, Reimer Kühn, and Ion-Olimpiu Stamatescu. "Learning structured data from unspecific reinforcement." Journal of Physics A: Mathematical and General 33, no. 39 (September 21, 2000): 6843–57. http://dx.doi.org/10.1088/0305-4470/33/39/302.

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Biehl, M., R. Kühn, and I.-O. Stamatescu. "Learning structured data from unspecific reinforcement." Journal of Physics A: Mathematical and General 34, no. 19 (May 2, 2001): 4267. http://dx.doi.org/10.1088/0305-4470/34/19/501.

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