very large data sets
Core Vector Machines: Fast SVM Training on Very Large Data Sets
... to data-intensive appli- cations involving very large data ...on large data sets, the number of support vectors may still be too large for real-time ...larger ...
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Fitting parametric random effects models in very large data sets with application to VHA national data
... generating very large data sets (VLDS) which require fitting complex models to answer questions of public health ...“very large” because of large numbers of study subjects ...
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Supervised sampling for clustering large data sets
... clustering large data sets has attracted a lot of current ...the data and then the assignment of all observations in those ...the data which are still constructed via sub-sampling but ...
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Analysis of large data sets using formal concept lattices
... Although large data sets may be difficult to deal with computationally, it is the number of formal concepts derived from a data set that is the key factor in de- termining if a concept lattice ...
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A System for Multilingual Sentiment Learning On Large Data Sets
... have very large data sets been used in empirical studies of sentiment ...multilingual data. Our implementation is fast, allowing a large number of documents to be classified in a ...
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Precision-Recall versus Accuracy and the Role of Large Data Sets
... in data mining in par- ticular have been long concerned with the effects of class imbalance or rarity of classes in training data (Weiss 2004; He and Garcia ...twenty-six data sets, they ...
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Parallel Computing for Mixed-Stable Modelling of Large Data Sets
... Therefore, in this research, we have chosen a more fine-grained parallelization at the ML target function (2) calculation level. In our previous work [2], we have shown that OpenMP [16] implementation of the parallel sum ...
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Privacy Preserving Data Analytics using Cryptographic Technique for Large Data Sets
... crucial very it comes at end of data. Nowadays, data has become the centric point of all industry and all are moving around data ...on data value rather than services. Morally, ...
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SEARCHING LARGE DATA SETS IN GRID COMPUTING
... On the technical issue about Grid adoption mentioned there are clearly many cultural barriers or non-technical as well [13]. Grid computing be resource sharing while the "corporate culture" of many organization ...
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Time series clustering in large data sets
... For our purpose the most interesting option is 10 x 10 neurons with the 10 dimensional feature vector with 2000 of learning iterations. There we can ob- serve several natural clusters, which grew out from the data ...
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Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
... for very large databases (by itself or as a starting point for more accurate methods) but to use it with confidence, one has to know when the method is indeed accurate ...
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Interpolative Multidimensional Scaling Techniques for the Identification of Clusters in Very Large Sequence Sets
... these large data sets (100,000+ sequences) is of particular interest for the purposes of sequence classification and identification of potential gene clusters and families, but such analysis cannot ...
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A Review on Big Data- Storage Techniques and Its Challenges
... Big data as the name indicates it is extremely large data sets collected from various sources like internet, camera, applications, and bank transactions and so ...such large quantity of ...
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Design and Research of Hadoop Distributed Cluster Based on Raspberry
... distributed data processing architecture consists of many elements, including HDFS, MapReduce, Pig, Hive, HBase and so ...of large distributed file systems; MapReduce is used for parallel computing of ...
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analyze and get meaningful results from the Big Data by using one of the most known data mining algorithms: k-Means. As data, we have utilized
... Big Data. As data, the mobile datasets containing the dynamics of several communities collected by the MIT Human Dynamics Lab is ...from data that are not readily ...of very large ...
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Strategies for MCR image analysis of large hyperspectral data sets
... ToF-SIMS data analysis, certain systems such as microarrays can still pose a challenge because of the number of separate samples (spots) involved and/or because it is desirable to analyse mm-scale ...requires ...
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INTERPRETOR: A Software Architecture for the Interpretation of Large and Noisy Data Sets
... raw data which is dependent on the expert’s attitude and ...raw data is meaningless because the data is at intervals much shorter than the ...
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The Use of Accelerometers to Quantify Collisions and Running Demands of Rugby Union Match Play
... general applicability of the regression models produced, 10-fold cross-validation was performed, with the root mean square error (RMSE) and the normalized root mean square error (NRMSE) (expressed as a percentage) ...
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Implementation and Analysis of Advanced Clustering Algorithms
... to data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information ...a very valuable data analysis technique, it has ...
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Training Data Sets Construction from Large Data Set for PCB Character Recognition
... Printed circuits board (PCB) is one of the key concepts in electronics used in different fields of industry. Usually, pattern recognition [1] and computer vision [2] algorithms are used to read and identify characters ...
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