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    BMCBI
    2008
    171views more  BMCBI 2008»
    A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more
    13 years 7 months ago
    A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more
    Download  www.biomedcentral.com
    Background: With the advent of high throughput biotechnology data acquisition platforms such as micro arrays, SNP chips and mass spectrometers, data sets with many more variables ...
    Harri T. Kiiveri
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    IJSNET
    2006
    107views more  IJSNET 2006»
    In-network aggregation trade-offs for data collection in wireless sensor networks
    13 years 7 months ago
    In-network aggregation trade-offs for data collection in wireless sensor networks
    Download  inrg.cse.ucsc.edu
    This paper explores in-network aggregation as a power-efficient mechanism for collecting data in wireless sensor networks. In particular, we focus on sensor network scenarios wher...
    Ignacio Solis, Katia Obraczka
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    BMCBI
    2005
    117views more  BMCBI 2005»
    An SVM-based system for predicting protein subnuclear localizations
    13 years 7 months ago
    An SVM-based system for predicting protein subnuclear localizations
    Download  www.biomedcentral.com
    Background: The large gap between the number of protein sequences in databases and the number of functionally characterized proteins calls for the development of a fast computatio...
    Zhengdeng Lei, Yang Dai
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    PVLDB
    2008
    138views more  PVLDB 2008»
    A skip-list approach for efficiently processing forecasting queries
    13 years 7 months ago
    A skip-list approach for efficiently processing forecasting queries
    Download  protocols.netlab.uky.edu
    Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support pred...
    Tingjian Ge, Stanley B. Zdonik
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    PROMISE
    2010
    160views Software Engineering» more  PROMISE 2010»
    On the value of learning from defect dense components for software defect prediction
    13 years 2 months ago
    On the value of learning from defect dense components for software defect prediction
    Download  menzies.us
    BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusi...
    Hongyu Zhang, Adam Nelson, Tim Menzies
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