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It allows for interactive usage through a fast and robust user interface, and batch processing for the analysis of large amounts of data.
Therefore, we used the results from utterance-based batch processing for the other features (note MLIFD does not use i-vectors).
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In addition, we present several partitioning strategies based on the unified framework, which are beneficial to the MapReduce-based batch processing for these unstructured data.
Their models functioned well for indoor and in-vehicle locations suggesting that batch processing for these environments may be feasible.
The behavior of STREAM is stable because it uses batch processing for all streams with the specific parameters.
For the eight-channel full batch processing, for each feature parameter, we combine the results from all eight channels using ROVER.
That's far faster than Hadoop, which uses what is known as "batch processing," for analyzing data.
Although, both STREAM and DCSTREAM utilize batch processing for stream clustering but there is no solution for monotonous data in STREAM.
Table 1 WER obtained using utterance-based batch processing for one-channel, two-channel, and eight-channel tasks SimData RealData Room 1 Room 2 Room 3 Avg.
Second, we provide a methodology, hierarchical metagrid alignment, to allow reliable and efficient batch processing for a set of microarray images.
Table 2 WER obtained using full batch processing for one-channel, two-channel, and eight-channel tasks SimData RealData Room 1 Room 2 Room 3 Avg.
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