发明申请
- 专利标题: BREADTH-FIRST, DEPTH-NEXT TRAINING OF COGNITIVE MODELS BASED ON DECISION TREES
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申请号: US16858900申请日: 2020-04-27
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公开(公告)号: US20210334709A1公开(公告)日: 2021-10-28
- 发明人: Nikolas Ioannou , Andreea Anghel , Thomas Parnell , Nikolaos Papandreou , Charalampos Pozidis
- 申请人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 申请人地址: US NY ARMONK
- 专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 当前专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 当前专利权人地址: US NY ARMONK
- 主分类号: G06N20/20
- IPC分类号: G06N20/20 ; G06N3/00 ; G06N5/00
摘要:
The present invention is notably directed to a computer-implemented method of training a cognitive model. The cognitive model includes decision trees as base learners. The method is performed using processing means to which a given cache memory is connected, so as to train the cognitive model based on training examples of a training dataset. The cognitive model is trained by running a hybrid tree building algorithm, so as to construct the decision trees and thereby associate the training examples to leaf nodes of the constructed decision trees, respectively. The hybrid tree building algorithm involves a first routine and a second routine. Each routine is designed to access the cache memory upon execution. The first routine involves a breadth-first search tree builder, while the second routine involves a depth-first search tree builder.
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