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  • book_chapters  (51)
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  • book_chapters  (51)
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  • 1
    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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  • 2
    Book chapter
    Book chapter
    World Scientific Publishing Co. Pte. Ltd.
    Language: English
    In: Biocomputing 2007, 2007, pp.169-180
    Description: Abstract Metabolomic databases are useless without accurate description of the biological study design and accompanying metadata reporting on the laboratory workflow from sample preparation to data processing. Here we report on the implementation of a database system that enables investigators to detail and set up a biological experiment, and that also steers laboratory workflows by direct access to the data acquisition instrument. SetupX utilizes orthogonal biological parameters such as genotype, organ, and treatment(s) for delineating the dimensions of a study which define the number of classes under investigation. Publicly available taxonomic and ontology repositories are utilized to ensure data integrity and logic consistency of class designs. Class descriptions are subsequently employed to schedule and randomize data acquisitions, and to deploy metabolite annotations carried out by the seamlessly integrated mass spectrometry database, BinBase. Annotated result data files are housed by SetupX for downloads and queries. Currently, 39 users have generated 48 studies, some of which are made public.
    Keywords: Computational Approaches To Metabolomics
    ISBN: 9789812772435
    Source: World Scientific Books (World Scientific Publishing Co.)
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  • 3
    Language: English
    Keywords: Metis-288697 ; Ir-81984
    ISBN: 9789814411608
    Source: NARCIS (National Academic Research and Collaborations Information System)
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  • 4
    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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  • 5
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Middle Ear Surgery, pp.167-174
    Keywords: Medicine & Public Health ; Otorhinolaryngology ; Head and Neck Surgery ; Medicine
    ISBN: 9783540222019
    ISBN: 3540222014
    Source: SpringerLink Books
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  • 6
    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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  • 7
    In: Encyclopedia of Multimedia Technology and Networking, Second Edition, Chapter 120, pp.880-886
    Description: Marketing research is the process of systematically gathering, analyzing, and interpreting data pertaining to the company’s market, customers, and competitors, with a view to improving marketing decisions. Multimedia technologies and the Internet have created opportunities previously unimagined in marketing research practice. Electronic or online marketing research takes one of two forms: research about the Internet and research on the Internet. Generally, marketing research activities cover the provision of relevant information to identify or solve marketing problems in the areas of market segmentation (e.g., selecting target markets or segments) as well as product (e.g., preference measurement for concept testing or new product development), pricing (e.g., identifying price thresholds), promotion (e.g., media and copy decisions), and distribution (e.g., location of retail outlets) decisions (Malhotra & Birks, 2005). This article aims to: • Review the impact of applying multimedia technologies to classic marketing research problems. • Present the different types of marketing research activities about the Internet as the most prominent application area of multimedia technologies. • Discuss the use of multimedia in online surveys in comparison to the traditional paper-and-pencil approach. The main contribution of the article is a discussion of advantages and challenges provided by innovative multimedia and network technologies for marketing researchers. Moreover, we present cues for improving the quality of surveys. The remainder of the article is structured as follows: First, we present examples of the application of multimedia technologies to illustrate the impact of multimedia on classic marketing research tasks. Subsequently, Web log mining, Web usage mining, and Web content mining are introduced as common marketing research fields directly concerned with research about the Internet. Then, benefits and challenges of online surveys are reviewed. Thereafter, we discuss response errors and ethical questions as crucial issues for the quality of data gained by online surveys. Finally, we draw conclusions and provide a spot on future developments.
    Keywords: Marketing Research; Web Mining; Online Surveys; Coverage Error; Sampling Error; Measurement Error; Non-response Error; Virtual Concept Testing
    ISBN: 9781605660141
    Source: IGI Global
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  • 8
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Lecture Notes in Computer Science, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II, pp.250-265
    Description: In this work, we introduce a powerful and general feature representation based on a locality sensitive hash scheme called random hyperplane hashing. We are addressing the problem of centrally learning (linear) classification models from data that is distributed on a number of clients, and subsequently deploying these models on the same clients. Our main goal is to balance the accuracy of individual classifiers and different kinds of costs related to their deployment, including communication costs and computational complexity. We hence systematically study how well schemes for sparse high-dimensional data adapt to the much denser representations gained by random hyperplane hashing, how much data has to be transmitted to preserve enough of the semantics of each document, and how the representations affect the overall computational complexity. This paper provides theoretical results in the form of error bounds and margin based bounds to analyze the performance of classifiers learnt over the hash-based representation. We also present empirical evidence to illustrate the attractive properties of random hyperplane hashing over the conventional baseline representation of bag of words with and without feature selection.
    Keywords: Computer Science ; Artificial Intelligence (Incl. Robotics) ; Database Management ; Information Storage and Retrieval ; Mathematical Logic and Formal Languages ; Algorithm Analysis and Problem Complexity ; Probability and Statistics in Computer Science ; Engineering ; Computer Science
    ISBN: 9783540874805
    ISBN: 3540874801
    Source: SpringerLink Books
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  • 9
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Lecture Notes in Computer Science, Knowledge Discovery in Databases: PKDD 2006: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases Berlin, Germany, September 18-22, 2006 Proceedings, pp.421-433
    Description: Subgroup discovery is a popular form of supervised rule learning, applicable to descriptive and predictive tasks. In this work we study two natural extensions of classical subgroup discovery to distributed settings. In the first variant the goal is to efficiently identify global subgroups, i.e. the rules an analysis would yield after collecting all the data at a single central database. In contrast, the second considered variant takes the locality of data explicitly into account. The aim is to find patterns that point out major differences between individual databases with respect to a specific property of interest (target attribute). We point out substantial differences between these novel learning problems and other kinds of distributed data mining tasks. These differences motivate new search and communication strategies, aiming at a minimization of computation time and communication costs. We present and empirically evaluate new algorithms for both considered variants.
    Keywords: Computer Science ; Artificial Intelligence (Incl. Robotics) ; Database Management ; Information Storage and Retrieval ; Probability and Statistics in Computer Science ; Document Preparation and Text Processing ; Mathematical Logic and Formal Languages ; Engineering ; Computer Science
    ISBN: 9783540453741
    ISBN: 3540453741
    Source: SpringerLink Books
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  • 10
    Language: English
    In: Lecture Notes in Computer Science, Data Integration in the Life Sciences: Second International Workshop, DILS 2005, San Diego, CA, USA, July 20-22, 2005. Proceedings, pp.224-239
    Description: Unbiased metabolomic surveys are used for physiological, clinical and genomic studies to infer genotype-phenotype relationships. Long term reusability of metabolomic data needs both correct metabolite annotations and consistent biological classifications. We have developed a system that combines mass spectrometric and biological metadata to achieve this goal. First, an XMLbased LIMS system enables entering biological metadata for steering laboratory workflows by generating ‘classes’ that reflect experimental designs. After data acquisition, a relational database system (BinBase) is employed for automated metabolite annotation. It consists of a manifold filtering algorithm for matching and generating database objects by utilizing mass spectral metadata such as ‘retention index’, ‘purity’, ‘signal/noise’, and the biological information class. Once annotations and quantitations are complete for a specific larger experiment, this information is fed back into the LIMS system to notify supervisors and users. Eventually, qualitative and quantitative results are released to the public for downloads or complex queries.
    Keywords: Computer Science ; Information Storage and Retrieval ; Health Informatics ; Database Management ; Information Systems Applications (Incl.Internet) ; Bioinformatics ; Computer Appl. in Life Sciences ; Biology ; Computer Science
    ISBN: 9783540279679
    ISBN: 3540279679
    Source: SpringerLink Books
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