Harbinger Machine Learning Toolkit Manual⋆
نویسندگان
چکیده
This manual is the primary guide to the Harbinger Machine Learning Toolkit (HMLT), which provides implementations for some well-known and frequently used machine learning classifiers. The main concerns in development of HMLT are correctness, effectiveness, transparency, modularity, and re-usability. At the moment, efficiency is not claimed to be a primary concern in any part of the toolkit. This is basically due to the fact that all supported classifier implementations use common representations and data structures, preventing further utilization and employment of some classifier-specific optimizations. However, we believe that the toolkit is quite robust and supposed to successfully execute under most circumstances.
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