- Patent Title: Modeling a subject process by machine learning with adaptive inputs
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Application No.: US14925101Application Date: 2015-10-28
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Publication No.: US10664743B2Publication Date: 2020-05-26
- Inventor: Ibuki Hara , Junya Shimizu , Michihiro Yokoyama
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Heslin Rothenberg Farley & Mesiti PC
- Agent Isaac I. Gooshaw, Esq.; George S. Blasiak, Esq.
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F8/35 ; G06N3/04

Abstract:
Methods, computer program products, and systems are presented. The methods include, for instance: modeling for a subject process by machine learning with adaptive inputs. In one embodiment, the modeling may include: generating a model by use of machine learning with training data from measurements of successive components of a process to be modeled in order to predict measurements of a succeeding component within a statistically meaningful prediction range; adjusting the generated model by use of machine learning with less-deviation inducing measurements from a preceding component in case the measurement of the succeeding component is out of the prediction range; and presenting the adjusted model as a prediction model for the process.
Public/Granted literature
- US20170124450A1 ADAPTIVE PREDICTION SYSTEM Public/Granted day:2017-05-04
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