首页>
外国专利>
SCALABLE, MEMORY-EFFICIENT MACHINE LEARNING AND PREDICTION FOR ENSEMBLES OF DECISION TREES FOR HOMOGENEOUS AND HETEROGENEOUS DATASETS
SCALABLE, MEMORY-EFFICIENT MACHINE LEARNING AND PREDICTION FOR ENSEMBLES OF DECISION TREES FOR HOMOGENEOUS AND HETEROGENEOUS DATASETS
展开▼
机译:均质和异质数据集的决策树树的可伸缩,高效内存的机器学习和预测
展开▼
页面导航
摘要
著录项
相似文献
摘要
Optimization of machine intelligence utilizes a systemic process through a plurality of computer architecture manipulation techniques that take unique advantage of efficiencies therein to minimize clock cycles and memory usage. The present invention is an application of machine intelligence which overcomes speed and memory issues in learning ensembles of decision trees in a single-machine environment. Such an application of machine intelligence includes inlining relevant statements by integrating function code into a caller's code, ensuring a contiguous buffering arrangement for necessary information to be compiled, and defining and enforcing type constraints on programming interfaces that access and manipulate machine learning data sets.
展开▼