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Though we test various classification methods, our framework does not depend on any one particular technique.
Compared to traditional transfer function based statistical downscaling methods our framework allows downscaling to basin level gridded rainfall rather than station specific precipitation.
The experimental results show that, compared to state-of-the-art VLAD methods, our framework achieves better retrieval accuracy with almost no compromise in time and memory efficiency.
By modeling website designs using well-defined structures and established analytical methods, our framework allows systematic and quantitative design evaluations and improvements.
Finally, although we exploited tree-based ensemble methods, our framework is general and other feature selection techniques could have been used as well.
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To overcome the limitation of conventional domain-prediction methods, our model framework regarded dc-pairs as the basic units of PPIs.
In addition to identifying biases in genome-wide evaluations of datasets or methods, our evaluation framework provides a way to normalize these biases out of the analysis.
For future work, we will evaluate alternative multivariate analysis methods in our framework.
We have, therefore, implemented robust general error detection and recovery methods within our framework (TextPipe) to help with unusual processing time, frequent crashes and other external problems, such as network connection timeouts or machine failures.
B-CNN, however, will be treated as an end-to-end model fine-tuning method in our framework.
The contribution presented in this paper is the development of a method for estimating the man-power cost (i.e., development time) for implementing hardware components and the integration of this method into our framework, so that HW/SW partitioning decisions can be wiser.
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