20-01-2022 дата публикации
Номер: US20220019902A1
Принадлежит:
Methods and servers for of training a decision-tree based Machine Learning Algorithm (MLA) are disclosed. During a given training iteration, the method includes generating prediction values using current generated trees, generating estimated gradient values by applying a loss function, generating a first plurality of noisy estimated gradient values based on the estimated gradient values, generating a plurality of noisy candidate trees using the first plurality of noisy estimated gradient values, applying a selection metric to select a target tree amongst the plurality of noisy candidate trees, generating a second plurality of noisy estimated gradient values based on the plurality of estimated gradient values, generating an iteration-specific tree based on the target tree and the second plurality of noisy estimated gradient values, and storing, the iteration-specific tree to be used in combination with the current generated trees. 2. The method of claim 1 , wherein the decision-tree based MLA is being trained for performing one of a regression task and a classification task during an in-use phase of the decision-tree based MLA.3. The method of claim 1 , wherein the loss function if one of a convex loss function and a non-convex loss function.4. The method of claim 3 , wherein the non-convex loss function includes a 0-1 loss function.5. The method of claim 1 , wherein the first noise-inducing function is:{'br': None, 'sub': 'i', 'img': {'@id': 'CUSTOM-CHARACTER-00014', '@he': '3.22mm', '@wi': '3.56mm', '@file': 'US20220019902A1-20220120-P00001.TIF', '@alt': 'custom-character', '@img-content': 'character', '@img-format': 'tif'}, 'sup': '−1', 'ζ=(0,2ϵβ)'}{'sub': 'i', 'sup': th', 'th, 'img': {'@id': 'CUSTOM-CHARACTER-00015', '@he': '3.22mm', '@wi': '3.56mm', '@file': 'US20220019902A1-20220120-P00001.TIF', '@alt': 'custom-character', '@img-content': 'character', '@img-format': 'tif'}, 'wherein: ζis a given noise value to be added to iestimated gradient value from the ...
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